Linear estimation of global average treatment effects

Stefan Faridani and Paul Niehaus

Papers from arXiv.org

Abstract: We study estimation of and inference for the average causal effect of treating every member of a population, as opposed to none, using an experiment that treats only some. Considering settings where spillovers can occur between any pair of units and decay slowly with distance, we derive the minimax rate over all linear estimators and experimental designs, which increases with the spatial rate of spillover decay. This rate of convergence can be achieved using an inverse probability weighting estimator when randomization clusters are large, but not otherwise. If the causal model is linear, however, an OLS-based estimator converges faster than IPW when clusters are small and is consistent even under unit-level randomization. We provide methods for radius selection and inference and apply these to the cash transfer experiment studied by Egger et al. (2022), obtaining a 22% larger estimated effect on consumption.

Date: 2022-09, Revised 2026-01
New Economics Papers: this item is included in nep-ecm and nep-exp
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