2018/06/28 by David Choi, Choi, David
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Economic Policies and Impacts #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Bayesian Inference
paper · pdf · doi:10.48550/arxiv.1806.11219
openalex publication_date 2018/06/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Exposure mappings are widely used to model potential outcomes in the presence of interference, where each unit's outcome may depend not only on its own treatment, but also on the treatment of other units as well. However, in practice these models may be only a crude proxy for social dynamics. In this work, we give estimands and estimators that are robust to the misspecification of an exposure model. In the first part, we require the treatment effect to be nonnegative (or "monotone") in both direct effects and spillovers. In the second part, we consider a weaker estimand ("contrasts attributable to treatment") which makes no restrictions on the interference at all.