Probabilities of Causation: Bounds and Identification

TL;DR

This paper develops sharp bounds for probabilities of causation using minimal assumptions, enhancing previous results and aiding decision-making and attribution in causal inference.

Contribution

It weakens data assumptions and derives theoretically sharp bounds on causation probabilities, improving causal attribution methods.

Findings

Derived sharp bounds on probabilities of causation

Weakened assumptions compared to previous models

Enhanced methods for causal attribution and decision making

Abstract

This paper deals with the problem of estimating the probability that one event was a cause of another in a given scenario. Using structural-semantical definitions of the probabilities of necessary or sufficient causation (or both), we show how to optimally bound these quantities from data obtained in experimental and observational studies, making minimal assumptions concerning the data-generating process. In particular, we strengthen the results of Pearl (1999) by weakening the data-generation assumptions and deriving theoretically sharp bounds on the probabilities of causation. These results delineate precisely how empirical data can be used both in settling questions of attribution and in solving attribution-related problems of decision making.