3 papers
stat.ME2026
Non-overlap Average Treatment Effect Bounds
Herbert P. Susmann, Alec McClean, Iván DÃaz
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
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…
stat.ME2025
Propensity score weighting across counterfactual worlds: longitudinal effects under positivity violations
Alec McClean, Iván DÃaz
When examining a contrast between two interventions, longitudinal causal inference studies frequently encounter positivity violations when one or both regimes are impossible to obs…