Causal Etiology of the Research of James M. Robins

Abstract

This issue of Statistical Science draws its inspiration from the work of James M. Robins. Jon Wellner, the Editor at the time, asked the two of us to edit a special issue that would highlight the research topics studied by Robins and the breadth and depth of Robins' contributions. Between the two of us, we have collaborated closely with Jamie for nearly 40 years. We agreed to edit this issue because we recognized that we were among the few in a position to relate the trajectory of his research career to date.

Published in at http://dx.doi.org/10.1214/14-STS505 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (21)

• External Validity: From Do-Calculus to Transportability Across Populations
• Doubly Robust Policy Evaluation and Learning
• On the definition of a confounder
• Structural Nested Models and G-estimation: The Partially Realized Promise
• Doubly Robust Policy Evaluation and Optimization
• Higher order influence functions and minimax estimation of nonlinear functionals
• On the Testability of Causal Models with Latent and Instrumental Variables
• Standardization and Control for Confounding in Observational Studies: A Historical Perspective
• Effect of mean on variance function estimation in nonparametric regression
• Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs
• Minimal sufficient causation and directed acyclic graphs
• Sharp bounds on the variance in randomized experiments
• Interference and Sensitivity Analysis
• Nonparametric Bounds and Sensitivity Analysis of Treatment Effects
• Higher Order Tangent Spaces and Influence Functions
• Probability distributions with summary graph structure
• General theory for interactions in sufficient cause models with dichotomous exposures
• On the Testable Implications of Causal Models with Hidden Variables
• Identifying Dynamic Sequential Plans
• A Uniformly Consistent Estimator of Causal Effects under the $k$-Triangle-Faithfulness Assumption
• Parameter and Structure Learning in Nested Markov Models

Cited by in corpus (2)

• Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects
• A robust score test in g-computation for covariate adjustment in randomized clinical trials leveraging different variance estimators via influence functions