Causal Al Market Size, Share, Growth Analysis, By Offering:(Platform), By Deployment:(Cloud, On-premises, Services, Consulting Services), By Vertical:(Healthcare & Lifesciences, BFSI, Retail & eCommerce, Tansportation & Logistics), By Region:(North America, US, Canada, Europe) - Industry Forecast 2025-2032

Report ID: UCMIG45A2109 | Region: Global | Published Date: Upcoming |
Pages: 165 |Tables: 0 |Figures: 0

Causal Al Market Insights

Market Overview:

At a predicted CAGR of 40.9% during the projection period, the global market for causal AI is expected to increase from USD 26 million in 2023 to USD 293 million by 2030. The importance of causal inference models in numerous sectors and operationalizing AI initiatives are the main market drivers.

Causal Al Market, Forecast & Y-O-Y Growth Rate, 2020 - 2028
ForecastGrowthRate
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This report is being written to illustrate the market opportunity by region and by segments, indicating opportunity areas for the vendors to tap upon. To estimate the opportunity, it was very important to understand the current market scenario and the way it will grow in future.

Production and consumption patterns are being carefully compared to forecast the market. Other factors considered to forecast the market are the growth of the adjacent market, revenue growth of the key market vendors, scenario-based analysis, and market segment growth.

The market size was determined by estimating the market through a top-down and bottom-up approach, which was further validated with industry interviews. Considering the nature of the market we derived the Internet Software & Services by segment aggregation, the contribution of the Internet Software & Services in Software & Services and vendor share.

To determine the growth of the market factors such as drivers, trends, restraints, and opportunities were identified, and the impact of these factors was analyzed to determine the market growth. To understand the market growth in detail, we have analyzed the year-on-year growth of the market. Also, historic growth rates were compared to determine growth patterns.

Segmentation Analysis:

The Causal Al Market is segmented by Offering:, Deployment:, Vertical:, Region:. We are analyzing the market of these segments to identify which segment is the largest now and in the future, which segment has the highest growth rate, and the segment which offers the opportunity in the future.

Causal Al Market Basis Point Share Analysis, 2021 Vs. 2028
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  • Based on Offering: the market is segmented as, Platform
  • Based on Deployment: the market is segmented as, Cloud, On-premises, Services, Consulting Services, Deployment & Integration, Training, Support, and Maintenance
  • Based on Vertical: the market is segmented as, Healthcare & Lifesciences, BFSI, Retail & eCommerce, Tansportation & Logistics, Manufacturing, Other Verticals
  • Based on Region: the market is segmented as, North America, US, Canada, Europe, UK, Germany, France, Rest of Europe, Rest of World, Israel, China, Japan, Rest of the RoW, KEY MARKET PLAYERS, IBM, CausaLens, Microsoft, Causaly, Google, Geminos, AWS, Aitia, Xplain Data, INCRMNTAL, Logility, Cognino.ai, H2O.ai, DataRobot, Cognizant, Scalnyx, Causality Link, Dynatrace, Parabole.ai, datma

Regional Analysis:

Causal Al Market is being analyzed by North America, Europe, Asia-Pacific (APAC), Latin America (LATAM), Middle East & Africa (MEA) regions. Key countries including the U.S., Canada, Germany, France, UK, Italy, Spain, China, India, Japan, Brazil, GCC Countries, and South Africa among others were analyzed considering various micro and macro trends.

Causal Al Market Attractiveness Analysis, By Region 2020-2028
AttractivenessAnalysis
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Causal Al Market : Risk Analysis

SkyQuest's expert analysts have conducted a risk analysis to understand the impact of external extremities on Causal Al Market. We analyzed how geopolitical influence, natural disasters, climate change, legal scenario, economic impact, trade & economic policies, social & ethnic concerns, and demographic changes might affect Causal Al Market's supply chain, distribution, and total revenue growth.

Competitive landscaping:

To understand the competitive landscape, we are analyzing key Causal Al Market vendors in the market. To understand the competitive rivalry, we are comparing the revenue, expenses, resources, product portfolio, region coverage, market share, key initiatives, product launches, and any news related to the Causal Al Market.

To validate our hypothesis and validate our findings on the market ecosystem, we are also conducting a detailed porter's five forces analysis. Competitive Rivalry, Supplier Power, Buyer Power, Threat of Substitution, and Threat of New Entry each force is analyzed by various parameters governing those forces.

Key Players Covered in the Report:

  • market for Causal AI Market is projected to grow from USD 26 million in 2023 to USD 293 million by 2030, at a CAGR of 40.9% during the forecast period. The importance of Causal Inference Models in Various Fields, Emergence of Causal AI as a Solution to Overcome the Limitations of Current AI, Operationalizing AI initiatives to drive the market growth.
  • Driver: The Importance of Causal Inference Models in Various Fields
  • Causal inference models are better suited for applications where accurate predictions are crucial. They are increasingly being adopted in the healthcare industry for diagnosis, treatment planning, and drug development due to their ability to identify causal relationships between medical conditions and treatments. The finance industry is also driving the growth of the causal AI market, with causal inference models being used for credit risk assessment, fraud detection, and portfolio optimization. Causal inference models provide a more transparent and interpretable approach to predictions, making them suitable for applications where explanations are necessary. This is important for industries such as healthcare and finance, where the ability to explain predictions is critical. In healthcare, causal inference models can identify causal relationships between medical conditions and treatments, leading to more accurate diagnosis, treatment planning, and drug development. In finance, causal inference models are used for credit risk assessment, fraud detection, and portfolio optimization. The ability of causal inference models to identify causal relationships and provide accurate and interpretable predictions is making them increasingly essential for businesses looking to make data-driven decisions.
  • Restraint: Acquiring and preparing high-quality data
  • Causal AI models require large amounts of high-quality data to train effectively, which can be challenging to acquire in many domains. In some cases, the data may not exist or may be difficult to obtain, while in other cases, the data may be incomplete, noisy, or biased, which can lead to inaccurate or unreliable models. In addition to the restraint of acquiring high-quality data, there are also challenges associated with preparing the data for use in causal AI and causal ML models. Causal AI models require data to be structured in a specific way, with clear cause-and-effect relationships between variables. This can require significant effort and expertise to achieve, particularly in complex domains where there may be many interacting factors and variables. To address these challenges, researchers and practitioners are exploring a range of techniques for acquiring and preparing high-quality data for use in causal AI models. One approach is to use data augmentation techniques to generate synthetic data that can be used to supplement or replace real-world data. Another approach is to use unsupervised learning techniques to identify patterns and relationships in the data that can be used to inform the development of causal AI models.
  • Opportunity: Causal AI is its potential to revolutionize the field of healthcare
  • Causal AI has enormous potential to revolutionize the healthcare industry by enabling researchers, physicians, and healthcare organizations to uncover and understand the complex relationships between different variables and diseases. One of the key opportunities of causal AI in healthcare is its ability to help identify the root causes of diseases, which can lead to more effective prevention and treatment strategies. Causal AI can also be used to analyze vast amounts of medical data, including electronic health records, patient history, and genetic data, to generate more accurate and personalized diagnoses and treatment plans. This can improve patient outcomes, reduce healthcare costs, and enhance the overall quality of care. Moreover, causal AI can also be used to predict disease outbreaks, identify high-risk populations, and optimize clinical trials, ultimately leading to more efficient and effective healthcare systems. Additionally, it can help in predicting and managing the impact of lifestyle factors and environmental exposures on health outcomes. By leveraging causal AI, healthcare organizations can unlock new insights and opportunities for research, precision medicine, and improved patient care. However, to fully realize the potential of causal AI in healthcare, it is essential to ensure that it is used responsibly and ethically, with a focus on patient privacy and data security.
  • Challenge: Causal Inference from Complex Data Sets
  • One of the significant challenges faced by causal AI is the ability to extract causality from complex and vast data sets. As the size and complexity of data sets increase, the identification of causal relationships becomes more difficult. The traditional statistical models used for causal inference may not be sufficient to handle the complexity of these data sets. Therefore, there is a need for more sophisticated methods and tools to identify causal relationships from large data sets. Additionally, in some cases, the causal relationship may not be immediately apparent and may require extensive analysis to identify. This presents a significant challenge for causal AI as it tries to provide accurate causal inferences in various industries.
  • By deployment, cloud to account for the largest market size during the forecast period
  • Cloud based deployment model provides organizations with a flexible, scalable, and cost-effective solution for accessing powerful causal inference tools. Cloud deployment allows organizations to easily scale their resources up or down as needed, without the need for significant upfront investments in hardware or software. Cloud-based causal AI platforms also offer the potential for greater accessibility, as they can be accessed from anywhere with an internet connection, enabling remote collaboration and data sharing. Cloud deployment also eliminates the need for organizations to manage and maintain their own hardware infrastructure, reducing IT resources and costs. Cloud providers typically offer robust security and compliance features, ensuring the security and privacy of data.
  • By offering, platform segment to account for the largest market size during the forecast period
  • Causal AI platforms typically leverage a range of statistical and machine learning techniques to identify causal relationships in data. These techniques may include regression analysis, propensity score matching, instrumental variable analysis, and other methods for causal inference. Platforms may also provide tools for data preprocessing and feature engineering to help users prepare their data for analysis. In addition to offering powerful tools for causal inference, many causal AI platforms also prioritize ease of use and accessibility. This may include providing user-friendly interfaces, visualizations, and tutorials to help users get started with the platform. As the demand for data-driven decision-making continues to grow across industries, the market for causal AI platforms is expected to expand rapidly in the coming years.
  • North America to account for the largest market size during the forecast period
  • North America plays a crucial role in the development and advancement of causal AI. Causal AI is becoming more popular as businesses and organizations seek more sophisticated analytics solutions to gain deeper insights and make better decisions. Governments in North America, such as the United States and Canada, have launched initiatives to promote the development and adoption of AI, providing funding and resources to support research and innovation in the field. In the United States, the National Institute of Standards and Technology (NIST) has been working on developing standards and guidelines for the use of AI in various industries, including healthcare and finance.
  • Recent Developments:
  • In February 2023, Dynatrace introduced new capabilities to Grail that enable boundless exploratory analysis by adding new data types and unlocking support for graph analytics. These capabilities enable Davis, the Dynatrace causal AI engine, to gather even more insights.
  • In January 2023, CausaLens released a new operating system for decision-making powered by causal AI. The system is designed to help organizations make more accurate predictions and optimize their business processes.
  • In December 2022, Microsoft launched a causal AI suite (DoWhy, EconML, Causica, and ShowWhy) for decision-making that enables developers and data scientists to build models that provide causal explanations for their predictions. The suite includes the DoWhy, EconML, and CausalML libraries, and is integrated with Azure Machine Learning and Azure Databricks.
  • In June 2022, Microsoft's collaboration with AWS to develop a new GitHub home for DoWhy will not only enhance the availability of the library but also help Microsoft gain a competitive edge in the causal machine learning space, showing a strategic move to leverage partnerships for growth.
  • In, September 2021, IBM launched its Causal AI offering, the Causal Inference 360 Toolkit. This innovative toolkit provides users with a range of powerful tools and algorithms for performing causal inference tasks, allowing businesses and researchers to gain valuable insights into complex systems and make better decisions.
  • KEY MARKET SEGMENTS
  • By Offering:
  • Platform
  • By Deployment:
  • Cloud
  • On-premises
  • Services
  • Consulting Services
  • Deployment & Integration
  • Training, Support, and Maintenance
  • By Vertical:
  • Healthcare & Lifesciences
  • BFSI
  • Retail & eCommerce
  • Tansportation & Logistics
  • Manufacturing
  • Other Verticals
  • By Region:
  • North America
  • US
  • Canada
  • Europe
  • UK
  • Germany
  • France
  • Rest of Europe
  • Rest of World
  • Israel
  • China
  • Japan
  • Rest of the RoW
  • KEY MARKET PLAYERS
  • IBM
  • CausaLens
  • Microsoft
  • Causaly
  • Google
  • Geminos
  • AWS
  • Aitia
  • Xplain Data
  • INCRMNTAL
  • Logility
  • Cognino.ai
  • H2O.ai
  • DataRobot
  • Cognizant
  • Scalnyx
  • Causality Link
  • Dynatrace
  • Parabole.ai
  • datma
  • B

SkyQuest's Expertise:

The Causal Al Market is being analyzed by SkyQuest's analysts with the help of 20+ scheduled Primary interviews from both the demand and supply sides. We have already invested more than 250 hours on this report and are still refining our date to provide authenticated data to your readers and clients. Exhaustive primary and secondary research is conducted to collect information on the market, peer market, and parent market.

Our cross-industry experts and revenue-impact consultants at SkyQuest enable our clients to convert market intelligence into actionable, quantifiable results through personalized engagement.

Scope Of Report

Report Attribute Details
The base year for estimation 2021
Historical data 2016 – 2022
Forecast period 2022 – 2028
Report coverage Revenue forecast, volume forecast, company ranking, competitive landscape, growth factors, and trends, Pricing Analysis
Segments covered
  • By Offering: - Platform
  • By Deployment: - Cloud, On-premises, Services, Consulting Services, Deployment & Integration, Training, Support, and Maintenance
  • By Vertical: - Healthcare & Lifesciences, BFSI, Retail & eCommerce, Tansportation & Logistics, Manufacturing, Other Verticals
  • By Region: - North America, US, Canada, Europe, UK, Germany, France, Rest of Europe, Rest of World, Israel, China, Japan, Rest of the RoW, KEY MARKET PLAYERS, IBM, CausaLens, Microsoft, Causaly, Google, Geminos, AWS, Aitia, Xplain Data, INCRMNTAL, Logility, Cognino.ai, H2O.ai, DataRobot, Cognizant, Scalnyx, Causality Link, Dynatrace, Parabole.ai, datma
Regional scope North America, Europe, Asia-Pacific (APAC), Latin America (LATAM), Middle East & Africa (MEA)
Country scope U.S., Canada, Germany, France, UK, Italy, Spain, China, India, Japan, Brazil, GCC Countries, South Africa
Key companies profiled
  • market for Causal AI Market is projected to grow from USD 26 million in 2023 to USD 293 million by 2030, at a CAGR of 40.9% during the forecast period. The importance of Causal Inference Models in Various Fields, Emergence of Causal AI as a Solution to Overcome the Limitations of Current AI, Operationalizing AI initiatives to drive the market growth.
  • Driver: The Importance of Causal Inference Models in Various Fields
  • Causal inference models are better suited for applications where accurate predictions are crucial. They are increasingly being adopted in the healthcare industry for diagnosis, treatment planning, and drug development due to their ability to identify causal relationships between medical conditions and treatments. The finance industry is also driving the growth of the causal AI market, with causal inference models being used for credit risk assessment, fraud detection, and portfolio optimization. Causal inference models provide a more transparent and interpretable approach to predictions, making them suitable for applications where explanations are necessary. This is important for industries such as healthcare and finance, where the ability to explain predictions is critical. In healthcare, causal inference models can identify causal relationships between medical conditions and treatments, leading to more accurate diagnosis, treatment planning, and drug development. In finance, causal inference models are used for credit risk assessment, fraud detection, and portfolio optimization. The ability of causal inference models to identify causal relationships and provide accurate and interpretable predictions is making them increasingly essential for businesses looking to make data-driven decisions.
  • Restraint: Acquiring and preparing high-quality data
  • Causal AI models require large amounts of high-quality data to train effectively, which can be challenging to acquire in many domains. In some cases, the data may not exist or may be difficult to obtain, while in other cases, the data may be incomplete, noisy, or biased, which can lead to inaccurate or unreliable models. In addition to the restraint of acquiring high-quality data, there are also challenges associated with preparing the data for use in causal AI and causal ML models. Causal AI models require data to be structured in a specific way, with clear cause-and-effect relationships between variables. This can require significant effort and expertise to achieve, particularly in complex domains where there may be many interacting factors and variables. To address these challenges, researchers and practitioners are exploring a range of techniques for acquiring and preparing high-quality data for use in causal AI models. One approach is to use data augmentation techniques to generate synthetic data that can be used to supplement or replace real-world data. Another approach is to use unsupervised learning techniques to identify patterns and relationships in the data that can be used to inform the development of causal AI models.
  • Opportunity: Causal AI is its potential to revolutionize the field of healthcare
  • Causal AI has enormous potential to revolutionize the healthcare industry by enabling researchers, physicians, and healthcare organizations to uncover and understand the complex relationships between different variables and diseases. One of the key opportunities of causal AI in healthcare is its ability to help identify the root causes of diseases, which can lead to more effective prevention and treatment strategies. Causal AI can also be used to analyze vast amounts of medical data, including electronic health records, patient history, and genetic data, to generate more accurate and personalized diagnoses and treatment plans. This can improve patient outcomes, reduce healthcare costs, and enhance the overall quality of care. Moreover, causal AI can also be used to predict disease outbreaks, identify high-risk populations, and optimize clinical trials, ultimately leading to more efficient and effective healthcare systems. Additionally, it can help in predicting and managing the impact of lifestyle factors and environmental exposures on health outcomes. By leveraging causal AI, healthcare organizations can unlock new insights and opportunities for research, precision medicine, and improved patient care. However, to fully realize the potential of causal AI in healthcare, it is essential to ensure that it is used responsibly and ethically, with a focus on patient privacy and data security.
  • Challenge: Causal Inference from Complex Data Sets
  • One of the significant challenges faced by causal AI is the ability to extract causality from complex and vast data sets. As the size and complexity of data sets increase, the identification of causal relationships becomes more difficult. The traditional statistical models used for causal inference may not be sufficient to handle the complexity of these data sets. Therefore, there is a need for more sophisticated methods and tools to identify causal relationships from large data sets. Additionally, in some cases, the causal relationship may not be immediately apparent and may require extensive analysis to identify. This presents a significant challenge for causal AI as it tries to provide accurate causal inferences in various industries.
  • By deployment, cloud to account for the largest market size during the forecast period
  • Cloud based deployment model provides organizations with a flexible, scalable, and cost-effective solution for accessing powerful causal inference tools. Cloud deployment allows organizations to easily scale their resources up or down as needed, without the need for significant upfront investments in hardware or software. Cloud-based causal AI platforms also offer the potential for greater accessibility, as they can be accessed from anywhere with an internet connection, enabling remote collaboration and data sharing. Cloud deployment also eliminates the need for organizations to manage and maintain their own hardware infrastructure, reducing IT resources and costs. Cloud providers typically offer robust security and compliance features, ensuring the security and privacy of data.
  • By offering, platform segment to account for the largest market size during the forecast period
  • Causal AI platforms typically leverage a range of statistical and machine learning techniques to identify causal relationships in data. These techniques may include regression analysis, propensity score matching, instrumental variable analysis, and other methods for causal inference. Platforms may also provide tools for data preprocessing and feature engineering to help users prepare their data for analysis. In addition to offering powerful tools for causal inference, many causal AI platforms also prioritize ease of use and accessibility. This may include providing user-friendly interfaces, visualizations, and tutorials to help users get started with the platform. As the demand for data-driven decision-making continues to grow across industries, the market for causal AI platforms is expected to expand rapidly in the coming years.
  • North America to account for the largest market size during the forecast period
  • North America plays a crucial role in the development and advancement of causal AI. Causal AI is becoming more popular as businesses and organizations seek more sophisticated analytics solutions to gain deeper insights and make better decisions. Governments in North America, such as the United States and Canada, have launched initiatives to promote the development and adoption of AI, providing funding and resources to support research and innovation in the field. In the United States, the National Institute of Standards and Technology (NIST) has been working on developing standards and guidelines for the use of AI in various industries, including healthcare and finance.
  • Recent Developments:
  • In February 2023, Dynatrace introduced new capabilities to Grail that enable boundless exploratory analysis by adding new data types and unlocking support for graph analytics. These capabilities enable Davis, the Dynatrace causal AI engine, to gather even more insights.
  • In January 2023, CausaLens released a new operating system for decision-making powered by causal AI. The system is designed to help organizations make more accurate predictions and optimize their business processes.
  • In December 2022, Microsoft launched a causal AI suite (DoWhy, EconML, Causica, and ShowWhy) for decision-making that enables developers and data scientists to build models that provide causal explanations for their predictions. The suite includes the DoWhy, EconML, and CausalML libraries, and is integrated with Azure Machine Learning and Azure Databricks.
  • In June 2022, Microsoft's collaboration with AWS to develop a new GitHub home for DoWhy will not only enhance the availability of the library but also help Microsoft gain a competitive edge in the causal machine learning space, showing a strategic move to leverage partnerships for growth.
  • In, September 2021, IBM launched its Causal AI offering, the Causal Inference 360 Toolkit. This innovative toolkit provides users with a range of powerful tools and algorithms for performing causal inference tasks, allowing businesses and researchers to gain valuable insights into complex systems and make better decisions.
  • KEY MARKET SEGMENTS
  • By Offering:
  • Platform
  • By Deployment:
  • Cloud
  • On-premises
  • Services
  • Consulting Services
  • Deployment & Integration
  • Training, Support, and Maintenance
  • By Vertical:
  • Healthcare & Lifesciences
  • BFSI
  • Retail & eCommerce
  • Tansportation & Logistics
  • Manufacturing
  • Other Verticals
  • By Region:
  • North America
  • US
  • Canada
  • Europe
  • UK
  • Germany
  • France
  • Rest of Europe
  • Rest of World
  • Israel
  • China
  • Japan
  • Rest of the RoW
  • KEY MARKET PLAYERS
  • IBM
  • CausaLens
  • Microsoft
  • Causaly
  • Google
  • Geminos
  • AWS
  • Aitia
  • Xplain Data
  • INCRMNTAL
  • Logility
  • Cognino.ai
  • H2O.ai
  • DataRobot
  • Cognizant
  • Scalnyx
  • Causality Link
  • Dynatrace
  • Parabole.ai
  • datma
  • B
Customization scope Free report customization (15% Free customization) with purchase. Addition or alteration to country, regional & segment scope.
Pricing and purchase options Reap the benefits of customized purchase options to fit your specific research requirements.

Objectives of the Study

  • To forecast the market size, in terms of value, for various segments with respect to five main regions, namely, North America, Europe, Asia-Pacific (APAC), Latin America (LATAM), Middle East & Africa (MEA)
  • To provide detailed information regarding the major factors influencing the growth of the Market (drivers, restraints, opportunities, and challenges)
  • To strategically analyze the micro markets with respect to the individual growth trends, future prospects, and contribution to the total market
  • To provide a detailed overview of the value chain and analyze market trends with the Porter's five forces analysis
  • To analyze the opportunities in the market for various stakeholders by identifying the high-growth Segments
  • To identify the key players and comprehensively analyze their market position in terms of ranking and core competencies, along with detailing the competitive landscape for the market leaders
  • To analyze competitive development such as joint ventures, mergers and acquisitions, new product launches and development, and research and development in the market

What does this Report Deliver?

  • Market Estimation for 20+ Countries
  • Historical data coverage: 2016 to 2022
  • Growth projections: 2022 to 2028
  • SkyQuest's premium market insights: Innovation matrix, IP analysis, Production Analysis, Value chain analysis, Technological trends, and Trade analysis
  • Customization on Segments, Regions, and Company Profiles
  • 100+ tables, 150+ Figures, 10+ matrix
  • Global and Country Market Trends
  • Comprehensive Mapping of Industry Parameters
  • Attractive Investment Proposition
  • Competitive Strategies Adopted by Leading Market Participants
  • Market drivers, restraints, opportunities, and its impact on the market
  • Regulatory scenario, regional dynamics, and insights of leading countries in each region
  • Segment trends analysis, opportunity, and growth
  • Opportunity analysis by region and country
  • Porter's five force analysis to know the market's condition
  • Pricing analysis
  • Parent market analysis
  • Product portfolio benchmarking

Table Of Content

Executive Summary

Market overview

  • Exhibit: Executive Summary – Chart on Market Overview
  • Exhibit: Executive Summary – Data Table on Market Overview
  • Exhibit: Executive Summary – Chart on Causal Al Market Characteristics
  • Exhibit: Executive Summary – Chart on Market by Geography
  • Exhibit: Executive Summary – Chart on Market Segmentation
  • Exhibit: Executive Summary – Chart on Incremental Growth
  • Exhibit: Executive Summary – Data Table on Incremental Growth
  • Exhibit: Executive Summary – Chart on Vendor Market Positioning

Parent Market Analysis

Market overview

Market size

  • Market Dynamics
    • Exhibit: Impact analysis of DROC, 2021
      • Drivers
      • Opportunities
      • Restraints
      • Challenges
  • SWOT Analysis

KEY MARKET INSIGHTS

  • Technology Analysis
    • (Exhibit: Data Table: Name of technology and details)
  • Pricing Analysis
    • (Exhibit: Data Table: Name of technology and pricing details)
  • Supply Chain Analysis
    • (Exhibit: Detailed Supply Chain Presentation)
  • Value Chain Analysis
    • (Exhibit: Detailed Value Chain Presentation)
  • Ecosystem Of the Market
    • Exhibit: Parent Market Ecosystem Market Analysis
    • Exhibit: Market Characteristics of Parent Market
  • IP Analysis
    • (Exhibit: Data Table: Name of product/technology, patents filed, inventor/company name, acquiring firm)
  • Trade Analysis
    • (Exhibit: Data Table: Import and Export data details)
  • Startup Analysis
    • (Exhibit: Data Table: Emerging startups details)
  • Raw Material Analysis
    • (Exhibit: Data Table: Mapping of key raw materials)
  • Innovation Matrix
    • (Exhibit: Positioning Matrix: Mapping of new and existing technologies)
  • Pipeline product Analysis
    • (Exhibit: Data Table: Name of companies and pipeline products, regional mapping)
  • Macroeconomic Indicators

COVID IMPACT

  • Introduction
  • Impact On Economy—scenario Assessment
    • Exhibit: Data on GDP - Year-over-year growth 2016-2022 (%)
  • Revised Market Size
    • Exhibit: Data Table on Causal Al Market size and forecast 2021-2027 ($ million)
  • Impact Of COVID On Key Segments
    • Exhibit: Data Table on Segment Market size and forecast 2021-2027 ($ million)
  • COVID Strategies By Company
    • Exhibit: Analysis on key strategies adopted by companies

MARKET DYNAMICS & OUTLOOK

  • Market Dynamics
    • Exhibit: Impact analysis of DROC, 2021
      • Drivers
      • Opportunities
      • Restraints
      • Challenges
  • Regulatory Landscape
    • Exhibit: Data Table on regulation from different region
  • SWOT Analysis
  • Porters Analysis
    • Competitive rivalry
      • Exhibit: Competitive rivalry Impact of key factors, 2021
    • Threat of substitute products
      • Exhibit: Threat of Substitute Products Impact of key factors, 2021
    • Bargaining power of buyers
      • Exhibit: buyers bargaining power Impact of key factors, 2021
    • Threat of new entrants
      • Exhibit: Threat of new entrants Impact of key factors, 2021
    • Bargaining power of suppliers
      • Exhibit: Threat of suppliers bargaining power Impact of key factors, 2021
  • Skyquest special insights on future disruptions
    • Political Impact
    • Economic impact
    • Social Impact
    • Technical Impact
    • Environmental Impact
    • Legal Impact

Market Size by Region

  • Chart on Market share by geography 2021-2027 (%)
  • Data Table on Market share by geography 2021-2027(%)
  • North America
    • Chart on Market share by country 2021-2027 (%)
    • Data Table on Market share by country 2021-2027(%)
    • USA
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Canada
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
  • Europe
    • Chart on Market share by country 2021-2027 (%)
    • Data Table on Market share by country 2021-2027(%)
    • Germany
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Spain
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • France
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • UK
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Rest of Europe
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
  • Asia Pacific
    • Chart on Market share by country 2021-2027 (%)
    • Data Table on Market share by country 2021-2027(%)
    • China
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • India
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Japan
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • South Korea
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Rest of Asia Pacific
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
  • Latin America
    • Chart on Market share by country 2021-2027 (%)
    • Data Table on Market share by country 2021-2027(%)
    • Brazil
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Rest of South America
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
  • Middle East & Africa (MEA)
    • Chart on Market share by country 2021-2027 (%)
    • Data Table on Market share by country 2021-2027(%)
    • GCC Countries
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • South Africa
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)
    • Rest of MEA
      • Exhibit: Chart on Market share 2021-2027 (%)
      • Exhibit: Market size and forecast 2021-2027 ($ million)

KEY COMPANY PROFILES

  • Competitive Landscape
    • Total number of companies covered
      • Exhibit: companies covered in the report, 2021
    • Top companies market positioning
      • Exhibit: company positioning matrix, 2021
    • Top companies market Share
      • Exhibit: Pie chart analysis on company market share, 2021(%)
  • market for Causal AI Market is projected to grow from USD 26 million in 2023 to USD 293 million by 2030, at a CAGR of 40.9% during the forecast period. The importance of Causal Inference Models in Various Fields, Emergence of Causal AI as a Solution to Overcome the Limitations of Current AI, Operationalizing AI initiatives to drive the market growth.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Driver: The Importance of Causal Inference Models in Various Fields
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Causal inference models are better suited for applications where accurate predictions are crucial. They are increasingly being adopted in the healthcare industry for diagnosis, treatment planning, and drug development due to their ability to identify causal relationships between medical conditions and treatments. The finance industry is also driving the growth of the causal AI market, with causal inference models being used for credit risk assessment, fraud detection, and portfolio optimization. Causal inference models provide a more transparent and interpretable approach to predictions, making them suitable for applications where explanations are necessary. This is important for industries such as healthcare and finance, where the ability to explain predictions is critical. In healthcare, causal inference models can identify causal relationships between medical conditions and treatments, leading to more accurate diagnosis, treatment planning, and drug development. In finance, causal inference models are used for credit risk assessment, fraud detection, and portfolio optimization. The ability of causal inference models to identify causal relationships and provide accurate and interpretable predictions is making them increasingly essential for businesses looking to make data-driven decisions.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Restraint: Acquiring and preparing high-quality data
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Causal AI models require large amounts of high-quality data to train effectively, which can be challenging to acquire in many domains. In some cases, the data may not exist or may be difficult to obtain, while in other cases, the data may be incomplete, noisy, or biased, which can lead to inaccurate or unreliable models. In addition to the restraint of acquiring high-quality data, there are also challenges associated with preparing the data for use in causal AI and causal ML models. Causal AI models require data to be structured in a specific way, with clear cause-and-effect relationships between variables. This can require significant effort and expertise to achieve, particularly in complex domains where there may be many interacting factors and variables. To address these challenges, researchers and practitioners are exploring a range of techniques for acquiring and preparing high-quality data for use in causal AI models. One approach is to use data augmentation techniques to generate synthetic data that can be used to supplement or replace real-world data. Another approach is to use unsupervised learning techniques to identify patterns and relationships in the data that can be used to inform the development of causal AI models.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Opportunity: Causal AI is its potential to revolutionize the field of healthcare
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Causal AI has enormous potential to revolutionize the healthcare industry by enabling researchers, physicians, and healthcare organizations to uncover and understand the complex relationships between different variables and diseases. One of the key opportunities of causal AI in healthcare is its ability to help identify the root causes of diseases, which can lead to more effective prevention and treatment strategies. Causal AI can also be used to analyze vast amounts of medical data, including electronic health records, patient history, and genetic data, to generate more accurate and personalized diagnoses and treatment plans. This can improve patient outcomes, reduce healthcare costs, and enhance the overall quality of care. Moreover, causal AI can also be used to predict disease outbreaks, identify high-risk populations, and optimize clinical trials, ultimately leading to more efficient and effective healthcare systems. Additionally, it can help in predicting and managing the impact of lifestyle factors and environmental exposures on health outcomes. By leveraging causal AI, healthcare organizations can unlock new insights and opportunities for research, precision medicine, and improved patient care. However, to fully realize the potential of causal AI in healthcare, it is essential to ensure that it is used responsibly and ethically, with a focus on patient privacy and data security.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Challenge: Causal Inference from Complex Data Sets
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • One of the significant challenges faced by causal AI is the ability to extract causality from complex and vast data sets. As the size and complexity of data sets increase, the identification of causal relationships becomes more difficult. The traditional statistical models used for causal inference may not be sufficient to handle the complexity of these data sets. Therefore, there is a need for more sophisticated methods and tools to identify causal relationships from large data sets. Additionally, in some cases, the causal relationship may not be immediately apparent and may require extensive analysis to identify. This presents a significant challenge for causal AI as it tries to provide accurate causal inferences in various industries.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • By deployment, cloud to account for the largest market size during the forecast period
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Cloud based deployment model provides organizations with a flexible, scalable, and cost-effective solution for accessing powerful causal inference tools. Cloud deployment allows organizations to easily scale their resources up or down as needed, without the need for significant upfront investments in hardware or software. Cloud-based causal AI platforms also offer the potential for greater accessibility, as they can be accessed from anywhere with an internet connection, enabling remote collaboration and data sharing. Cloud deployment also eliminates the need for organizations to manage and maintain their own hardware infrastructure, reducing IT resources and costs. Cloud providers typically offer robust security and compliance features, ensuring the security and privacy of data.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • By offering, platform segment to account for the largest market size during the forecast period
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Causal AI platforms typically leverage a range of statistical and machine learning techniques to identify causal relationships in data. These techniques may include regression analysis, propensity score matching, instrumental variable analysis, and other methods for causal inference. Platforms may also provide tools for data preprocessing and feature engineering to help users prepare their data for analysis. In addition to offering powerful tools for causal inference, many causal AI platforms also prioritize ease of use and accessibility. This may include providing user-friendly interfaces, visualizations, and tutorials to help users get started with the platform. As the demand for data-driven decision-making continues to grow across industries, the market for causal AI platforms is expected to expand rapidly in the coming years.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • North America to account for the largest market size during the forecast period
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • North America plays a crucial role in the development and advancement of causal AI. Causal AI is becoming more popular as businesses and organizations seek more sophisticated analytics solutions to gain deeper insights and make better decisions. Governments in North America, such as the United States and Canada, have launched initiatives to promote the development and adoption of AI, providing funding and resources to support research and innovation in the field. In the United States, the National Institute of Standards and Technology (NIST) has been working on developing standards and guidelines for the use of AI in various industries, including healthcare and finance.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Recent Developments:
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • In February 2023, Dynatrace introduced new capabilities to Grail that enable boundless exploratory analysis by adding new data types and unlocking support for graph analytics. These capabilities enable Davis, the Dynatrace causal AI engine, to gather even more insights.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • In January 2023, CausaLens released a new operating system for decision-making powered by causal AI. The system is designed to help organizations make more accurate predictions and optimize their business processes.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • In December 2022, Microsoft launched a causal AI suite (DoWhy, EconML, Causica, and ShowWhy) for decision-making that enables developers and data scientists to build models that provide causal explanations for their predictions. The suite includes the DoWhy, EconML, and CausalML libraries, and is integrated with Azure Machine Learning and Azure Databricks.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • In June 2022, Microsoft's collaboration with AWS to develop a new GitHub home for DoWhy will not only enhance the availability of the library but also help Microsoft gain a competitive edge in the causal machine learning space, showing a strategic move to leverage partnerships for growth.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • In, September 2021, IBM launched its Causal AI offering, the Causal Inference 360 Toolkit. This innovative toolkit provides users with a range of powerful tools and algorithms for performing causal inference tasks, allowing businesses and researchers to gain valuable insights into complex systems and make better decisions.
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • KEY MARKET SEGMENTS
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • By Offering:
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Platform
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • By Deployment:
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Cloud
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • On-premises
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Services
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Consulting Services
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Deployment & Integration
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Training, Support, and Maintenance
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • By Vertical:
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Healthcare & Lifesciences
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • BFSI
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Retail & eCommerce
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Tansportation & Logistics
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Manufacturing
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Other Verticals
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • By Region:
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • North America
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • US
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Canada
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Europe
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • UK
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Germany
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • France
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Rest of Europe
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Rest of World
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Israel
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • China
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Japan
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Rest of the RoW
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • KEY MARKET PLAYERS
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • IBM
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • CausaLens
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Microsoft
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Causaly
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Google
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Geminos
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • AWS
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Aitia
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Xplain Data
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • INCRMNTAL
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Logility
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Cognino.ai
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • H2O.ai
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • DataRobot
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Cognizant
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Scalnyx
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Causality Link
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Dynatrace
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • Parabole.ai
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • datma
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments
  • B
    • Exhibit Company Overview
    • Exhibit Business Segment Overview
    • Exhibit Financial Updates
    • Exhibit Key Developments

Methodology

For the Causal Al Market, our research methodology involved a mixture of primary and secondary data sources. Key steps involved in the research process are listed below:

1. Information Procurement: This stage involved the procurement of Market data or related information via primary and secondary sources. The various secondary sources used included various company websites, annual reports, trade databases, and paid databases such as Hoover's, Bloomberg Business, Factiva, and Avention. Our team did 45 primary interactions Globally which included several stakeholders such as manufacturers, customers, key opinion leaders, etc. Overall, information procurement was one of the most extensive stages in our research process.

2. Information Analysis: This step involved triangulation of data through bottom-up and top-down approaches to estimate and validate the total size and future estimate of the Causal Al Market.

3. Report Formulation: The final step entailed the placement of data points in appropriate Market spaces in an attempt to deduce viable conclusions.

4. Validation & Publishing: Validation is the most important step in the process. Validation & re-validation via an intricately designed process helped us finalize data points to be used for final calculations. The final Market estimates and forecasts were then aligned and sent to our panel of industry experts for validation of data. Once the validation was done the report was sent to our Quality Assurance team to ensure adherence to style guides, consistency & design.

Analyst Support

Customization Options

With the given market data, our dedicated team of analysts can offer you the following customization options are available for the Causal Al Market:

Product Analysis: Product matrix, which offers a detailed comparison of the product portfolio of companies.

Regional Analysis: Further analysis of the Causal Al Market for additional countries.

Competitive Analysis: Detailed analysis and profiling of additional Market players & comparative analysis of competitive products.

Go to Market Strategy: Find the high-growth channels to invest your marketing efforts and increase your customer base.

Innovation Mapping: Identify racial solutions and innovation, connected to deep ecosystems of innovators, start-ups, academics, and strategic partners.

Category Intelligence: Customized intelligence that is relevant to their supply Markets will enable them to make smarter sourcing decisions and improve their category management.

Public Company Transcript Analysis: To improve the investment performance by generating new alpha and making better-informed decisions.

Social Media Listening: To analyze the conversations and trends happening not just around your brand, but around your industry as a whole, and use those insights to make better Marketing decisions.

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FAQs

The market for Causal Al was estimated to be valued at US$ XX Mn in 2021.

The Causal Al Market is estimated to grow at a CAGR of XX% by 2028.

The Causal Al Market is segmented on the basis of Offering:, Deployment:, Vertical:, Region:.

Based on region, the Causal Al Market is segmented into North America, Europe, Asia Pacific, Middle East & Africa and Latin America.

The key players operating in the Causal Al Market are market for Causal AI Market is projected to grow from USD 26 million in 2023 to USD 293 million by 2030, at a CAGR of 40.9% during the forecast period. The importance of Causal Inference Models in Various Fields, Emergence of Causal AI as a Solution to Overcome the Limitations of Current AI, Operationalizing AI initiatives to drive the market growth. , Driver: The Importance of Causal Inference Models in Various Fields , Causal inference models are better suited for applications where accurate predictions are crucial. They are increasingly being adopted in the healthcare industry for diagnosis, treatment planning, and drug development due to their ability to identify causal relationships between medical conditions and treatments. The finance industry is also driving the growth of the causal AI market, with causal inference models being used for credit risk assessment, fraud detection, and portfolio optimization. Causal inference models provide a more transparent and interpretable approach to predictions, making them suitable for applications where explanations are necessary. This is important for industries such as healthcare and finance, where the ability to explain predictions is critical. In healthcare, causal inference models can identify causal relationships between medical conditions and treatments, leading to more accurate diagnosis, treatment planning, and drug development. In finance, causal inference models are used for credit risk assessment, fraud detection, and portfolio optimization. The ability of causal inference models to identify causal relationships and provide accurate and interpretable predictions is making them increasingly essential for businesses looking to make data-driven decisions. , Restraint: Acquiring and preparing high-quality data , Causal AI models require large amounts of high-quality data to train effectively, which can be challenging to acquire in many domains. In some cases, the data may not exist or may be difficult to obtain, while in other cases, the data may be incomplete, noisy, or biased, which can lead to inaccurate or unreliable models. In addition to the restraint of acquiring high-quality data, there are also challenges associated with preparing the data for use in causal AI and causal ML models. Causal AI models require data to be structured in a specific way, with clear cause-and-effect relationships between variables. This can require significant effort and expertise to achieve, particularly in complex domains where there may be many interacting factors and variables. To address these challenges, researchers and practitioners are exploring a range of techniques for acquiring and preparing high-quality data for use in causal AI models. One approach is to use data augmentation techniques to generate synthetic data that can be used to supplement or replace real-world data. Another approach is to use unsupervised learning techniques to identify patterns and relationships in the data that can be used to inform the development of causal AI models. , Opportunity: Causal AI is its potential to revolutionize the field of healthcare , Causal AI has enormous potential to revolutionize the healthcare industry by enabling researchers, physicians, and healthcare organizations to uncover and understand the complex relationships between different variables and diseases. One of the key opportunities of causal AI in healthcare is its ability to help identify the root causes of diseases, which can lead to more effective prevention and treatment strategies. Causal AI can also be used to analyze vast amounts of medical data, including electronic health records, patient history, and genetic data, to generate more accurate and personalized diagnoses and treatment plans. This can improve patient outcomes, reduce healthcare costs, and enhance the overall quality of care. Moreover, causal AI can also be used to predict disease outbreaks, identify high-risk populations, and optimize clinical trials, ultimately leading to more efficient and effective healthcare systems. Additionally, it can help in predicting and managing the impact of lifestyle factors and environmental exposures on health outcomes. By leveraging causal AI, healthcare organizations can unlock new insights and opportunities for research, precision medicine, and improved patient care. However, to fully realize the potential of causal AI in healthcare, it is essential to ensure that it is used responsibly and ethically, with a focus on patient privacy and data security. , Challenge: Causal Inference from Complex Data Sets , One of the significant challenges faced by causal AI is the ability to extract causality from complex and vast data sets. As the size and complexity of data sets increase, the identification of causal relationships becomes more difficult. The traditional statistical models used for causal inference may not be sufficient to handle the complexity of these data sets. Therefore, there is a need for more sophisticated methods and tools to identify causal relationships from large data sets. Additionally, in some cases, the causal relationship may not be immediately apparent and may require extensive analysis to identify. This presents a significant challenge for causal AI as it tries to provide accurate causal inferences in various industries. , By deployment, cloud to account for the largest market size during the forecast period , Cloud based deployment model provides organizations with a flexible, scalable, and cost-effective solution for accessing powerful causal inference tools. Cloud deployment allows organizations to easily scale their resources up or down as needed, without the need for significant upfront investments in hardware or software. Cloud-based causal AI platforms also offer the potential for greater accessibility, as they can be accessed from anywhere with an internet connection, enabling remote collaboration and data sharing. Cloud deployment also eliminates the need for organizations to manage and maintain their own hardware infrastructure, reducing IT resources and costs. Cloud providers typically offer robust security and compliance features, ensuring the security and privacy of data. , By offering, platform segment to account for the largest market size during the forecast period , Causal AI platforms typically leverage a range of statistical and machine learning techniques to identify causal relationships in data. These techniques may include regression analysis, propensity score matching, instrumental variable analysis, and other methods for causal inference. Platforms may also provide tools for data preprocessing and feature engineering to help users prepare their data for analysis. In addition to offering powerful tools for causal inference, many causal AI platforms also prioritize ease of use and accessibility. This may include providing user-friendly interfaces, visualizations, and tutorials to help users get started with the platform. As the demand for data-driven decision-making continues to grow across industries, the market for causal AI platforms is expected to expand rapidly in the coming years. , North America to account for the largest market size during the forecast period , North America plays a crucial role in the development and advancement of causal AI. Causal AI is becoming more popular as businesses and organizations seek more sophisticated analytics solutions to gain deeper insights and make better decisions. Governments in North America, such as the United States and Canada, have launched initiatives to promote the development and adoption of AI, providing funding and resources to support research and innovation in the field. In the United States, the National Institute of Standards and Technology (NIST) has been working on developing standards and guidelines for the use of AI in various industries, including healthcare and finance. , Recent Developments: , In February 2023, Dynatrace introduced new capabilities to Grail that enable boundless exploratory analysis by adding new data types and unlocking support for graph analytics. These capabilities enable Davis, the Dynatrace causal AI engine, to gather even more insights. , In January 2023, CausaLens released a new operating system for decision-making powered by causal AI. The system is designed to help organizations make more accurate predictions and optimize their business processes. , In December 2022, Microsoft launched a causal AI suite (DoWhy, EconML, Causica, and ShowWhy) for decision-making that enables developers and data scientists to build models that provide causal explanations for their predictions. The suite includes the DoWhy, EconML, and CausalML libraries, and is integrated with Azure Machine Learning and Azure Databricks. , In June 2022, Microsoft's collaboration with AWS to develop a new GitHub home for DoWhy will not only enhance the availability of the library but also help Microsoft gain a competitive edge in the causal machine learning space, showing a strategic move to leverage partnerships for growth. , In, September 2021, IBM launched its Causal AI offering, the Causal Inference 360 Toolkit. This innovative toolkit provides users with a range of powerful tools and algorithms for performing causal inference tasks, allowing businesses and researchers to gain valuable insights into complex systems and make better decisions. , KEY MARKET SEGMENTS, By Offering: , Platform , By Deployment: , Cloud , On-premises , Services , Consulting Services , Deployment & Integration , Training, Support, and Maintenance , By Vertical: , Healthcare & Lifesciences , BFSI , Retail & eCommerce , Tansportation & Logistics , Manufacturing , Other Verticals , By Region: , North America , US , Canada , Europe , UK , Germany , France , Rest of Europe , Rest of World , Israel , China , Japan , Rest of the RoW , KEY MARKET PLAYERS , IBM , CausaLens , Microsoft , Causaly , Google , Geminos , AWS , Aitia , Xplain Data , INCRMNTAL , Logility , Cognino.ai , H2O.ai , DataRobot , Cognizant , Scalnyx , Causality Link , Dynatrace , Parabole.ai , datma , B.

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