Average direct and indirect causal effects under interference

Estimating average causal effects under general interference, with application to a social network experiment

Yuchen Hu, Shuangning Li and Stefan Wager

Biometrika, 2022, vol. 109, issue 4, 1165-1172

Abstract: SummaryWe propose a definition for the average indirect effect of a binary treatment in the potential outcomes model for causal inference under cross-unit interference. Our definition is analogous to the standard definition of the average direct effect and can be expressed without needing to compare outcomes across multiple randomized experiments. We show that the proposed indirect effect satisfies a decomposition theorem stating that in a Bernoulli trial, the sum of the average direct and indirect effects always corresponds to the effect of a policy intervention that infinitesimally increases treatment probabilities. We also consider a number of parametric models for interference and find that our nonparametric indirect effect remains a natural estimand when re-expressed in the context of these models.

Keywords: Causal inference; Interference; Potential outcome; Randomized trial (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (10)

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