3 papers
stat.ME2020
Approximate Bayesian Bootstrap Procedures to Estimate Multilevel Treatment Effects in Observational Studies with Application to Type 2 Diabetes Treatment Regimens
Anthony D. Scotina, Andrew R. Zullo, Robert J. Smith +1
Randomized clinical trials are considered the gold standard for estimating causal effects. Nevertheless, in studies that are aimed at examining adverse effects of interventions, su…
stat.ME2018
Matching Algorithms for Causal Inference with Multiple Treatments
Anthony D. Scotina, Roee Gutman
Randomized clinical trials (RCTs) are ideal for estimating causal effects, because the distributions of background covariates are similar in expectation across treatment groups. Wh…
stat.ME2018
Matching Estimators for Causal Effects of Multiple Treatments
Anthony D. Scotina, Francesca L. Beaudoin, Roee Gutman
Matching estimators for average treatment effects are widely used in the binary treatment setting, in which missing potential outcomes are imputed as the average of observed outcom…