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Using Exposure Mappings as Side Information in Experiments with Interference
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Using Exposure Mappings as Side Information in Experiments with Interference
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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.
Forward citations
Cited by 2 Pith papers
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A Design-Based Approach to Testing and Inference in (Quasi-)Experiments with Spillovers
A correctly specified exposure map implies design-based orthogonality conditions that let researchers estimate and test the map's functional form and tuning parameters using only the known randomization.
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A Design-Based Approach to Testing and Inference in (Quasi-)Experiments with Spillovers
A correctly specified exposure map implies design-side orthogonality conditions, so the exposure radius can be estimated by GMM and tested by overidentification — rejecting the 2 km radius in the GiveDirectly experiment.
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