6 papers
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…
Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest
Jeffrey Näf, Junhyung Park, Herbert Susmann
The conditional average treatment effect (CATE) is a commonly targeted statistical parameter for measuring the effect of a treatment conditional on covariates. However, the CATE wi…
The Counterfactual Combine: A Causal Framework for Player Evaluation
Herbert P. Susmann, Antonio D'Alessandro
Evaluating sports players based on their performance shares core challenges with evaluating healthcare providers based on patient outcomes. Drawing on recent advances in healthcare…
Bayesian Projection of Extant Refugee and Asylum Seeker Populations
Herbert Susmann, Adrian E. Raftery
Estimates of future migration patterns are of broad interest in demography. Forced migration, including refugee and asylum seekers, plays an important role in overall migration pat…
Computationally and statistically efficient estimation of time-smoothed counterfactual curves
Herbert P. Susmann, Nicholas T. Williams, Richard Liu +2
Longitudinal causal inference is concerned with defining, identifying, and estimating the effect of a time-varying intervention on a time-varying outcome that is indexed by a follo…
Asymptotically Efficient Data-adaptive Penalized Shrinkage Estimation with Application to Causal Inference
Herbert P. Susmann, Yiting Li, Mara A. McAdams-DeMarco +2
A rich literature exists on constructing non-parametric estimators with optimal asymptotic properties. In addition to asymptotic guarantees, it is often of interest to design estim…