Causal AI and its applications
Causal AI is a field of artificial intelligence that focuses on understanding and modeling causal relationships between variables. Unlike traditional machine learning, which primarily focuses on correlation, causal AI aims to uncover the underlying mechanisms that drive changes in outcomes. Key concepts in causal AI:
1)Causal inference:The process of drawing conclusions about cause-and-effect relationships from data.
2)Structural causal models (SCMs):Graphical representations of causal relationships between variables.
3)Interventional causal inference:The process of predicting the impact of interventions or changes to a system.
4)Counterfactual reasoning:The process of reasoning about what would have happened if something had been different.
Benefits of causal AI:
Improved decision-making:Causal AI can help organizations make more informed decisions by understanding the true drivers of outcomes.
Enhanced model interpretability:Causal models are more transparent and easier to understand than black-box models.
Reduced bias:Causal AI can help identify and mitigate biases in data and models.
Novel insights:Causal AI can uncover hidden relationships between variables that are not apparent from correlation analysis.
Applications of causal AI:
Healthcare:Identifying the causes of diseases and the effectiveness of treatments.
Economics:Understanding the impact of economic policies on various outcomes.
Marketing:Optimizing marketing campaigns by understanding the causal impact of different marketing channels.
Social sciences:Studying the causes of social phenomena.
Causal AI is a rapidly growing field with the potential to revolutionize many industries. By understanding the underlying causes of events, we can make more informed decisions and develop more effective interventions.
In marketing, Causal AI accurately attributes sales and conversions to specific marketing channels and campaigns. Marketers use causal AI to identify the causal impact of each marketing touchpoint on customer behavior.
Optimized campaign strategies also enable effective allocation of marketing budgets.
In Oil&Gas or mining, Mid-run failures in drilling equipment notably impact the bottom line of oil service companies.
Increasing the rate of penetration can increase the number of mid-run breakages, which are incredibly expensive and very time-consuming to fix. A digital twin of drilling runs helps to understand the trade-off between the rate of penetration and the likelihood of a fault, given certain run parameters.
many companies had previously used traditional ML methods to predict the likelihood of failures but could not explain why a breakage was expected and, more importantly, actions that could be taken to prevent future failures.
We offer an automated, scalable end-to-end causal AI approach that uses a combination of domain-expertise and data-driven causal discovery to accurately represent each run digitally.
The solution allows the customer to ask key ‘what-if’ questions for all of the drilling runs to understand actions that could be taken to optimize each run.solution allows the customer to access each drilling run through a custom interactive application that we call a decisionApp. The app enables the customer to ask ‘what-if’ questions and see how changes to run parameters would affect the likelihood of equipment breakages and the rate of penetration.
This capability allows customers to evaluate different scenarios and plan their drilling runs in a way that minimizes the risk of breakage and optimizes the completion time.
In Systematic Asset Management, Researchers use factor models to obtain unbiased estimates of the premia harvested by assets exposed to certain risk characteristics. These estimates are unbiased only if the factor models are correctly specified.
Choosing the correct model specification requires knowledge of the causal graph that characterizes the underlying data-generating process. However, following the current econometric canon, factor researchers choose their model specifications using associational (non-causal) arguments, such as the model’s explanatory power, instead of applying causal inference procedures, such as do-calculus. As a result, factor investing models are likely misspecified, and the estimates of risk premia are biased.
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