Speaker: Prof. Jin Tian Date: 15-10-2026 11am-12pm (GMT) Location: Department of Computer Science, CS1.04, University of Warwick, Coventry, UK

Causal Effect Measures for Causal Explanation and Decision-Making

Abstract

Understanding cause-and-effect relations is a fundamental challenge across the data sciences. Traditional measures, such as the Average Causal Effect (ACE), summarize causal relationships through averages but often obscure important heterogeneity across individuals or subpopulations. In this talk, we introduce a set of new causal effect measures designed to capture and interpret effect heterogeneity for better causal explanation and decision-making. We characterize causal heterogeneity through statistical moments (variance, skewness, and kurtosis), present new formulations for the probabilities of causation, and introduce new metrics for causal decision-making under multiple actions. We outline identification and bounding results for these measures under common causal assumptions and illustrate their use in real-world applications.

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About Prof. Jin Tian

Jin Tian is a Professor of ML at Mohamed bin Zayed University of Artificial Intelligence and serves as the editor-in-chief of the Journal of Causal Inference. His research focuses on causal modelling, probabilistic graphical models, and adversarial robustness, including work on Probabilities of causation and Causal effect identification in cluster DAGs. He is an action editor for the Journal of Machine Learning Research and previously spent nearly two decades on the CS faculty at Iowa State University. Tian earned a PhD in CS from UCLA after completing a BS in Physics at Tsinghua University.