Inference on Average Treatment Effects for Continuous Treatments
Bias-corrected Neyman Sample Average Treatment Effect Estimator
Classic Neyman Sample Average Treatment Effect Estimator
Covariate-Adjusted Variance Estimation
Generate example data with five covariates
Generate sample data with six covariates
Make matrix of treatment assignment probabilities
non-bipartite matching with treatment assignment caliper
Conduct inference on the sample average treatment effect for a matched (observational) dataset with a continuous treatment. Equipped with calipered non-bipartite matching, bias-corrected sample average treatment effect estimation, and covariate-adjusted variance estimation. Matching, estimation, and inference methods are described in Frazier, Heng and Zhou (2024) <doi:10.48550/arXiv.2409.11701>.
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