6 papers · 1 filter
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…
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…
Doubly Robust Nonparametric Efficient Estimation for Provider Evaluation
Herbert Susmann, Yiting Li, Mara A. McAdams-DeMarco +2
Provider profiling has the goal of identifying healthcare providers with exceptional patient outcomes. When evaluating providers, adjustment is necessary to control for differences…
Longitudinal Generalizations of the Average Treatment Effect on the Treated for Multi-valued and Continuous Treatments
Herbert Susmann, Nicholas T. Williams, Kara E. Rudolph +1
The Average Treatment Effect on the Treated (ATT) is a common causal parameter defined as the average effect of a binary treatment among the subset of the population receiving trea…