313 citations · 1.6k across the 58 of their papers we have counts for
9 papers · 1 filter
Efficient estimation of optimal regimes under a no direct effect assumption
Lin Liu, Zach Shahn, James M. Robins +1
We derive new estimators of an optimal joint testing and treatment regime under the no direct effect (NDE) assumption that a given laboratory, diagnostic, or screening test has no…
Identification In Missing Data Models Represented By Directed Acyclic Graphs
Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser +1
Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution. Many approaches to inference on the target…
Generalizing causal inferences from randomized trials: counterfactual and graphical identification
Issa J. Dahabreh, James M. Robins, Sebastien J-P. A. Haneuse +1
When engagement with a randomized trial is driven by factors that affect the outcome or when trial engagement directly affects the outcome independent of treatment, the average tre…
Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population
Issa J. Dahabreh, James M. Robins, Sebastien J-P. A. Haneuse +4
Extending (generalizing or transporting) causal inferences from a randomized trial to a target population requires ``generalizability'' or ``transportability'' assumptions, which s…
Study designs for extending causal inferences from a randomized trial to a target population
Issa J. Dahabreh, Sebastien J-P. A. Haneuse, James M. Robins +4
We examine study designs for extending (generalizing or transporting) causal inferences from a randomized trial to a target population. Specifically, we consider nested trial desig…
Characterization of parameters with a mixed bias property
Andrea Rotnitzky, Ezequiel Smucler, James M. Robins
In this article we study a class of parameters with the so-called `mixed bias property'. For parameters with this property, the bias of the semiparametric efficient one step estima…
On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
Lin Liu, Rajarshi Mukherjee, James M. Robins
For many causal effect parameters of interest, doubly robust machine learning (DRML) estimators are the state-of-the-art, incorporating the good prediction performance…
A unifying approach for doubly-robust regularized estimation of causal contrasts
Ezequiel Smucler, Andrea Rotnitzky, James M. Robins
We consider inference about a scalar parameter under a non-parametric model based on a one-step estimator computed as a plug in estimator plus the empirical mean of an estimator of…
Separable Effects for Causal Inference in the Presence of Competing Events
Mats J. Stensrud, Jessica G. Young, Vanessa Didelez +2
In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a…