Population Interference in Panel Experiments

• Activity Number: 444 - Recent Advances in Statistical Methodology for Big Data
• Type: Contributed
• Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
• Sponsor: IMS
• Author(s): Kevin Wu Han* and Iavor Bojinov and Guillaume Basse
• Companies: Department of Statistics, Stanford University and Harvard Business School and Department of MS&E and Department of Statistics, Stanford University
• Keywords: Causal Inference; Interference; Experimental Design; Potential Outcomes; Dynamic Causal Effects

Abstract

The complications produced by population interference in randomized experiments are now readily recognized, and partial remedies are well known. Much less understood is the impact of population interference in panel experiments where treatment is sequentially randomized in the population, and the outcomes are observed at each time step. This paper proposes a general framework for studying population interference in panel experiments and presents new finite population estimation and inference results. Our findings suggest that, under mild assumptions, the addition of a temporal dimension to an experiment alleviates some of the challenges of population interference for certain estimands. In contrast, we show that the presence of carryover effects -- that is, when past treatments may affect future outcomes -- exacerbates the problem. Revisiting the special case of standard experiments with population interference, we prove a central limit theorem under weaker conditions than previous results in the literature and highlight the trade-off between flexibility in the design and the interference structure.