2022/09/28 by Stefan Faridani, Faridani, Stefan, Paul Niehaus +1 · 1 citation
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Econometrics (econ.EM) #FOS: Economics and business #Health Systems, Economic Evaluations, Quality of Life #Healthcare Policy and Management
paper · pdf · doi:10.48550/arxiv.2209.14181
openalex publication_date 2022/09/28 · openalex created_date 2023/02/14 · openalex updated_date 2026/07/28
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.