3 citations · 4 across the 12 of their papers we have counts for
14 papers
More efficient transportation of causal effects using mediators
Xinyi Zhang, Kara E. Rudolph, James Russell Pike +3
We develop flexible semiparametric estimators of the average treatment effect transported to a target population that leverage mediators to achieve identification, and prior knowle…
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…
Time-smoothed inverse probability weighted estimation of effects of generalized time-varying treatment strategies on repeated outcomes truncated by death
Sean McGrath, Takuya Kawahara, Joshua Petimar +4
Researchers are often interested in estimating effects of generalized time-varying treatment strategies on the mean of an outcome at one or more selected follow-up times of interes…
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…
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…
Nonparametric estimation of an optimal treatment rule with fused randomized trials and missing effect modifiers
Nicholas Williams, Kara Rudolph, Iván Díaz
A fundamental principle of clinical medicine is that a treatment should only be administered to those patients who would benefit from it. Treatment strategies that assign treatment…
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…
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…
General targeted machine learning for modern causal mediation analysis
Richard Liu, Nicholas T. Williams, Kara E. Rudolph +1
Causal mediation analyses investigate the mechanisms through which causes exert their effects, and are therefore central to scientific progress. The literature on the non-parametri…
A Surrogate Endpoint Based Provisional Approval Causal Roadmap
Peter B. Gilbert, James Peng, Larry Han +12
For many rare diseases with no approved preventive interventions, promising interventions exist, yet it has been difficult to conduct a pivotal phase 3 trial that could provide dir…
Identification and estimation of mediational effects of longitudinal modified treatment policies
Brian Gilbert, Katherine L. Hoffman, Nicholas Williams +3
We demonstrate a comprehensive semiparametric approach to causal mediation analysis, addressing the complexities inherent in settings with longitudinal and continuous treatments, c…
Two-Step Targeted Minimum-Loss Based Estimation for Non-Negative Two-Part Outcomes
Nicholas T. Williams, Richard Liu, Katherine L. Hoffman +3
Non-negative two-part outcomes are defined as outcomes with a density function that have a zero point mass but are otherwise positive. Examples, such as healthcare expenditure and…
Leveraging Neural Networks to Profile Health Care Providers with Application to Medicare Claims
Wenbo Wu, Fan Li, Richard Liu +5
Encompassing numerous nationwide, statewide, and institutional initiatives in the United States, provider profiling has evolved into a major health care undertaking with ubiquitous…