The paper introduces Horvitz-Thompson estimators for edge-level causal effects under dyadic interference, a three-fold cross-fitting scheme to enable machine learning covariate adjustment, and a calibration step ensuring no asymptotic efficiency loss.
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Design-based edge-level causal inference with machine learning assisted covariate adjustment
The paper introduces Horvitz-Thompson estimators for edge-level causal effects under dyadic interference, a three-fold cross-fitting scheme to enable machine learning covariate adjustment, and a calibration step ensuring no asymptotic efficiency loss.