Efficient Treatment Effect Estimation in Observational Studies under Heterogeneous Partial Interference

In many observational studies in social science and medical applications, subjects or individuals are connected, and one unit's treatment and attributes may affect another unit's treatment and outcome, violating the stable unit treatment value assumption (SUTVA) and resulting in interference. To enable feasible inference, many previous works assume the ``exchangeability'' of interfering units, under which the effect of interference is captured by the number or ratio of treated neighbors. However, in many applications with distinctive units, interference is heterogeneous. In this paper, we focus on the partial interference setting, and restrict units to be exchangeable conditional on observable characteristics. Under this framework, we propose generalized augmented inverse propensity weighted (AIPW) estimators for general causal estimands that include direct treatment effects and spillover effects. We show that they are consistent, asymptotically normal, semiparametric efficient, and robust to heterogeneous interference as well as model misspecifications. We also apply our method to the Add Health dataset and find that smoking behavior exhibits interference on academic outcomes.