Causal Etiology of the Research of James M. Robins

2014/11/01 by Thomas S. Richardson, Andrea Rotnitzky · 1 citation
Mathematics · #Advanced Causal Inference Techniques #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #stat.ME

paper · pdf · doi:10.1214/14-sts505

published as Statistical Science 2014, Vol. 29, No. 4, 459-484 · 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)

openalex publication_date 2014/11/01 · arxiv created 2015/03/10 · arxiv updated 2015/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

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.

Citations

• Identifying Dynamic Sequential Plans
• Direct and Indirect Effects
• On the Testable Implications of Causal Models with Hidden Variables
• Parameter and Structure Learning in Nested Markov Models
• Doubly Robust Policy Evaluation and Learning
• External Validity: From Do-Calculus to Transportability Across Populations
• Estimation of Regression Coefficients When Some Regressors are not Always Observed
• Covariate adjustment for two‐sample treatment comparisons in randomized clinical trials: A principled yet flexible approach
• Eight Myths About Causality and Structural Equation Models
• Estimating causal effects of treatments in randomized and nonrandomized studies.
• Doubly Robust Estimation in Missing Data and Causal Inference Models
• Estimating high-dimensional directed acyclic graphs with the PC-algorithm

Cited by

• James Robins [wikipedia]

Related

• Reconciling Causality and Statistics
• Potential Outcomes and Decision Theoretic Foundations for Statistical Causality: Response to Richardson and Robins
• Challenges in Statistics: A Dozen Challenges in Causality and Causal Inference
• The causal foundations of applied probability and statistics
• Handbook of Causal Analysis for Social Research. Morgan, Stephen L. (ed.) (2013)