Regression adjustments for estimating the global treatment effect in experiments with interference

Abstract

Standard estimators of the global average treatment effect can be biased in the presence of interference. This paper proposes regression adjustment estimators for removing bias due to interference in Bernoulli randomized experiments. We use a fitted model to predict the counterfactual outcomes of global control and global treatment. Our work differs from standard regression adjustments in that the adjustment variables are constructed from functions of the treatment assignment vector, and that we allow the researcher to use a collection of any functions correlated with the response, turning the problem of detecting interference into a feature engineering problem. We characterize the distribution of the proposed estimator in a linear model setting and connect the results to the standard theory of regression adjustments under SUTVA. We then propose an estimator that allows for flexible machine learning estimators to be used for fitting a nonlinear interference functional form. We propose conducting statistical inference via bootstrap and resampling methods, which allow us to sidestep the complicated dependences implied by interference and instead rely on empirical covariance structures. Such variance estimation relies on an exogeneity assumption akin to the standard unconfoundedness assumption invoked in observational studies. In simulation experiments, our methods are better at debiasing estimates than existing inverse propensity weighted estimators based on neighborhood exposure modeling. We use our method to reanalyze an experiment concerning weather insurance adoption conducted on a collection of villages in rural China.

arXiv comment38 pages, 7 figures

References in corpus (10)

• Community detection in graphs
• The Anatomy of the Facebook Social Graph
• Social Structure of Facebook Networks
• Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's critique
• On regression adjustments in experiments with several treatments
• Lasso adjustments of treatment effect estimates in randomized experiments
• High-dimensional regression adjustments in randomized experiments
• Elements of estimation theory for causal effects in the presence of network interference
• Central limit theorems via Stein's method for randomized experiments under interference
• Causal Diagrams for Interference