Design-Based Inference for Spatial Experiments with Interference

Excited to share “Design-Based Inference for Spatial Experiments with Interference”, joint with Peter M. Aronow and Ye Wang: arxiv

In settings with complex spatial effects and interference, the paper defines a type of marginal effect, the “average marginalized response,” that has a clear interpretation and can be identified with a spatial experiment and a simple contrast.

It took time to work out details for robust inference, and finally got there with Ye working out reasonable conditions that justify the spatial HAC variance estimator, and then by connecting to a breakthrough CLT result from Ogburn et al. (2020; arxiv link).

We are working on the public release of the R package and also a more didactic paper that walks through applications. Stay tuned for those.


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