7 papers
General Probabilities of Causation with Causal Knowledge
Xin Shu, Zhen Lei, Ang Li
Probabilities of causation (PoCs) characterize individual causal responses that cannot be directly observed and therefore generally require partial identification. Tian and Pearl f…
General sample size analysis for probabilities of causation: a delta method approach
Tianyuan Cheng, Ruirui Mao, Judea Pearl +1
Probabilities of causation (PoCs), such as the probability of necessity and sufficiency (PNS), are important tools for decision making but are generally not point identifiable. Exi…
Bounding Probabilities of Causation with Partial Causal Diagrams
Yuxuan Xie, Ang Li
Probabilities of causation are fundamental to individual-level explanation and decision making, yet they are inherently counterfactual and not point-identifiable from data in gener…
Recover Experimental Data with Selection Bias using Counterfactual Logic
Jingyang He, Shuai Wang, Ang Li
Selection bias, arising from the systematic inclusion or exclusion of certain samples, poses a significant challenge to the validity of causal inference. While Bareinboim et al. in…
Identification of Probabilities of Causation: from Recursive to Closed-Form Bounds
Xin Shu, Shuai Wang, Ang Li
Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making. However, existing analytical results are largely confined…
Estimating Probabilities of Causation with Machine Learning Models
Shuai Wang, Ang Li
Probabilities of causation play a crucial role in modern decision-making. This paper addresses the challenge of predicting probabilities of causation for subpopulations with insuff…
Causality in the Can: Diet Coke's Impact on Fatness
Yicheng Qi, Ang Li
Artificially sweetened beverages like Diet Coke are often considered better alternatives to sugary drinks, but the debate over their impact on health, particularly in relation to o…