313 citations · 1.6k across the 58 of their papers we have counts for
8 papers · 1 filter
Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
Lin Liu, Rajarshi Mukherjee, James M. Robins
This is the rejoinder to the discussion by Kennedy, Balakrishnan and Wasserman on the paper "On nearly assumption-free tests of nominal confidence interval coverage for causal para…
Double-robust and efficient methods for estimating the causal effects of a binary treatment
James Robins, Mariela Sued, Quanhong Lei-Gomez +1
We consider the problem of estimating the effects of a binary treatment on a continuous outcome of interest from observational data in the absence of confounding by unmeasured fact…
An Interventionist Approach to Mediation Analysis
James M. Robins, Thomas S. Richardson, Ilya Shpitser
Judea Pearl's insight that, when errors are assumed independent, the Pure (aka Natural) Direct Effect (PDE) is non-parametrically identified via the Mediation Formula was `path-bre…
Multivariate Counterfactual Systems And Causal Graphical Models
Ilya Shpitser, Thomas S. Richardson, James M. Robins
Among Judea Pearl's many contributions to Causality and Statistics, the graphical d-separation} criterion, the do-calculus and the mediation formula stand out. In this chapter we s…
Estimation of local treatment effects under the binary instrumental variable model
Linbo Wang, Yuexia Zhang, Thomas S. Richardson +1
Instrumental variables are widely used to deal with unmeasured confounding in observational studies and imperfect randomized controlled trials. In these studies, researchers often…
Conditional separable effects
Mats J. Stensrud, James M. Robins, Aaron Sarvet +2
Researchers are often interested in treatment effects on outcomes that are only defined conditional on a post-treatment event status. For example, in a study of the effect of diffe…
Generalized interpretation and identification of separable effects in competing event settings
Mats J. Stensrud, Miguel A. Hernán, Eric J. Tchetgen Tchetgen +3
In competing event settings, a counterfactual contrast of cause-specific cumulative incidences quantifies the total causal effect of a treatment on the event of interest. However,…
A Formal Causal Interpretation of the Case-Crossover Design
Zach Shahn, Miguel A. Hernan, James M. Robins
The case-crossover design (Maclure, 1991) is widely used in epidemiology and other fields to study causal effects of transient treatments on acute outcomes. However, its validity a…