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stat.ME2025
Non-overlap Average Treatment Effect Bounds
Herbert P. Susmann, Alec McClean, Iván Díaz
The average treatment effect (ATE), the mean difference in potential outcomes under treatment and control, is a canonical causal effect. Overlap, which says that all subjects have…
stat.ME2025
Propensity score weighting across counterfactual worlds: longitudinal effects under positivity violations
Alec McClean, Iván Díaz
When examining a contrast between two interventions, longitudinal causal inference studies frequently encounter positivity violations when one or both regimes are impossible to obs…
stat.ME2024
Comparing causal parameters with many treatments and positivity violations
Alec McClean, Yiting Li, Sunjae Bae +3
Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, with each ta…