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Activity Number: 563 - Mechanisms of Interference: New Strategies for Identification and Estimation
Type: Invited
Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
Sponsor: Social Statistics Section
Abstract #326709 Presentation
Title: Causal Inference Under Unmodeled and All-Encompassing Interference
Author(s): Fredrik Sävje*
Companies: Yale University
Keywords: treatment effects; causal inference; interference; SUTVA

It has been established that causal effects can be estimated under interference either when the structure of the interference is known or when interference is restricted so that a limited number of units are interfering with each other. This paper investigates whether causal inference is possible when neither of those conditions hold. I ask whether we can accurately estimate causal effects in randomized experiments when all pairs of units are interfering and the structure of the interference is left unmodeled. I show that common experimental estimators are consistent with respect to an average treatment effect if we partially restrict the strength of the interference but otherwise allow it to have an arbitrary and unknown form. In particular, consistency follows if the number of pairs of units with "strong" interference grows at a sufficiently slow rate. The results do not require us to identify these pairs.

Authors who are presenting talks have a * after their name.

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