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Bounding Causes of Effects with Mediators

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  • Philip Dawid
  • Macartan Humphreys
  • Monica Musio

Abstract

Suppose X and Y are binary exposure and outcome variables, and we have full knowledge of the distribution of Y, given application of X. From this we know the average causal effect of X on Y. We are now interested in assessing, for a case that was exposed and exhibited a positive outcome, whether it was the exposure that caused the outcome. The relevant "probability of causation", PC, typically is not identified by the distribution of Y given X, but bounds can be placed on it, and these bounds can be improved if we have further information about the causal process. Here we consider cases where we know the probabilistic structure for a sequence of complete mediators between X and Y. We derive a general formula for calculating bounds on PC for any pattern of data on the mediators (including the case with no data). We show that the largest and smallest upper and lower bounds that can result from any complete mediation process can be obtained in processes with at most two steps. We also consider homogeneous processes with many mediators. PC can sometimes be identified as 0 with negative data, but it cannot be identified at 1 even with positive data on an infinite set of mediators. The results have implications for learning about causation from knowledge of general processes and of data on cases.

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  • Philip Dawid & Macartan Humphreys & Monica Musio, 2019. "Bounding Causes of Effects with Mediators," Papers 1907.00399, arXiv.org.
    • Handle: RePEc:arx:papers:1907.00399
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    References listed on IDEAS

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    1. Andrew Gelman & Guido Imbens, 2013. "Why ask Why? Forward Causal Inference and Reverse Causal Questions," NBER Working Papers 19614, National Bureau of Economic Research, Inc.
    2. White, Howard, 2009. "Theory-Based Impact Evaluation," 3ie Publications 2009-3, International Initiative for Impact Evaluation (3ie).
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