James M. Robins

Locally Robust Semiparametric Estimation

Econometrica · 2022 · 122 citations

Many economic and causal parameters depend on nonparametric or high dimensional first steps. We give a general construction of locally robust/orthogonal moment functions for GMM, where first steps have no effect, locally, on average moment functions. Using these orthogonal moments reduces model selection and regularization bias, as is important in many applications, especially for machine learning first steps. Also, associated standard errors are robust to misspecification when there is the same…

Separable Effects for Causal Inference in the Presence of Competing Events

Journal of the American Statistical Association · 2020 · 101 citations

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 treatment on an event of interest. The separable direct effect is the treatment effect on the event of interest not mediated by its effect on the competing event. The separable indirect effect is the treatment effect on the event of interest only through its effect on the competing event. Similar to Robins and Richards…

An Interventionist Approach to Mediation Analysis

ACM eBooks · 2022 · 48 citations

chapter Share on An Interventionist Approach to Mediation Analysis Authors: James M. Robins Harvard T. H. Chan School of Public Health Harvard T. H. Chan School of Public HealthSearch about this author , Thomas S. Richardson University of Washington University of WashingtonSearch about this author , Ilya Shpitser Johns Hopkins University Johns Hopkins UniversitySearch about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 713–764ht…

Sensitivity analysis using bias functions for studies extending inferences from a randomized trial to a target population

Statistics in Medicine · 2023-02-27 · 32 citations

Extending (i.e., generalizing or transporting) causal inferences from a randomized trial to a target population requires assumptions that randomized and nonrandomized individuals are exchangeable conditional on baseline covariates. These assumptions are made on the basis of background knowledge, which is often uncertain or controversial, and need to be subjected to sensitivity analysis. We present simple methods for sensitivity analyses that directly parameterize violations of the assumptions us…

Multivariate Counterfactual Systems and Causal Graphical Models

ACM eBooks · 2022 · 22 citations

chapter Share on Multivariate Counterfactual Systems and Causal Graphical Models Authors: Ilya Shpitser Johns Hopkins University Johns Hopkins UniversitySearch about this author , Thomas S. Richardson University of Washington University of WashingtonSearch about this author , James M. Robins Harvard T. H. Chan School of Public Health Harvard T. H. Chan School of Public HealthSearch about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 P…