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Probabilities of Causation: Adequate Size of Experimental and Observational Samples
Ang Li · Ruirui Mao · Judea Pearl

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in Workshop on neuro Causal and Symbolic AI (nCSI) »
Event URL: https://openreview.net/forum?id=cJEPY_BTH0 »

The probabilities of causation are commonly used to solve decision-making problems. Tian and Pearl derived sharp bounds for the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of necessity (PN) using experimental and observational data. The assumption is that one is in possession of a large enough sample to permit an accurate estimation of the experimental and observational distributions. In this study, we present a method for determining the sample size needed for such estimation, when a given confidence interval (CI) is specified. We further show by simulation that the proposed sample size delivered stable estimations of the bounds of PNS.

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Author Information

Ang Li (University of California, Los Angeles)
Ruirui Mao
Judea Pearl (UCLA)

Judea Pearl is a professor of computer science and statistics at UCLA. He is a graduate of the Technion, Israel, and has joined the faculty of UCLA in 1970, where he conducts research in artificial intelligence, causal inference and philosophy of science. Pearl has authored three books: Heuristics (1984), Probabilistic Reasoning (1988), and Causality (2000;2009), the latter won the Lakatos Prize from the London School of Economics. He is a member of the National Academy of Engineering, the American Academy of Arts and Sciences, and a Fellow of the IEEE, AAAI and the Cognitive Science Society. Pearl received the 2008 Benjamin Franklin Medal from the Franklin Institute and the 2011 Rumelhart Prize from the Cognitive Science Society. In 2012, he received the Technion's Harvey Prize and the ACM Alan M. Turing Award.

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