Causal Etiology of the Research of James M. Robins
arXiv:1503.02894 · doi:10.1214/14-STS505
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.
arXiv commentPublished 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 (30)
- Estimating high-dimensional directed acyclic graphs with the PC-algorithm
- External Validity: From Do-Calculus to Transportability Across Populations
- Doubly Robust Policy Evaluation and Learning
- On the definition of a confounder
- Learning high-dimensional directed acyclic graphs with latent and selection variables
- - and -Learning Methods for Estimating Optimal Dynamic Treatment Regimes
- 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
- Statistics, Causality and Bell's Theorem
- Standardization and Control for Confounding in Observational Studies: A Historical Perspective
- Effect of mean on variance function estimation in nonparametric regression
- Markov properties for mixed graphs
- 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
- Markovian acyclic directed mixed graphs for discrete data
- 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
- The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA?
- A Uniformly Consistent Estimator of Causal Effects under the -Triangle-Faithfulness Assumption
- Parameter and Structure Learning in Nested Markov Models
- A proof of Bell's inequality in quantum mechanics using causal interactions
- On the testability of the CAR assumption