Treatment Effect Estimation with Observational Network Data using Machine Learning

Corinne Emmenegger, Meta-Lina Spohn, Timon Elmer, Peter Bühlmann

Publication date: 29 June 2022

Abstract: Causal inference methods for treatment effect estimation usually assume independent experimental units. However, this assumption is often questionable because experimental units may interact. We develop augmented inverse probability weighting (AIPW) for estimation and inference of causal treatment effects on dependent observational data. Our framework covers very general cases of spillover effects induced by units interacting in networks. We use plugin machine learning to estimate infinite-dimensional nuisance components leading to a consistent treatment effect estimator that converges at the parametric rate and asymptotically follows a Gaussian distribution. We apply our AIPW method to the Swiss StudentLife Study data to investigate the effect of hours spent studying on exam performance accounting for the students' social network.

Has companion code repository: https://github.com/corinne-rahel/networkaipw

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