Statistics and Data Science Seminar: Casual Inference in the Presence of Interference: Estimation and Testing Problems

Abstract: In causal inference, "interference" refers to a scenario where the treatment assigned to one unit affects the observed outcomes of other units. In a wide variety of applied settings, such interference effects not only exist but are of considerable interest. In this talk, I will present some tools I have developed to conduct statistical inference in the presence of such interference.

1. Estimation:

I will begin by discussing estimation problems, focusing on a study that examines large-sample asymptotics for treatment effect estimation under network interference, where the interference graph is a random draw from a graphon. For direct effects, we demonstrate that popular estimators in this setting are significantly more accurate than previously suggested. For indirect effects, we propose a new consistent estimator in a setting where no other consistent estimators currently exist.

2. Testing:

If time permits, I will then discuss testing problems. I will present a study focused on testing for interference in A/B testing with increasing allocation. Specifically, we introduce two permutation tests designed to detect the existence of interference, each valid under different assumptions. These procedures have been implemented at LinkedIn to detect potential interference across all their marketplace experiments.

Host: Robert Lunde