World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
6443
World Ranking
10677
National Ranking
4466

Mathematics

D-Index
37
Citations
6516
World Ranking
2478
National Ranking
1035

Overview

Thomas S. Richardson is affiliated with the University of Washington in the United States. Their research primarily focuses on the field of Mathematics, with extensive work related to Statistics and Probability.

The scientist's research spans several subfields including:

  • Statistics and Probability
  • Artificial Intelligence
  • Economics and Econometrics
  • Atomic and Molecular Physics, and Optics
  • General Health Professions

Key topics explored in Richardson's work include:

  • Advanced Causal Inference Techniques
  • Statistical Methods and Bayesian Inference
  • Statistical Methods and Inference
  • Bayesian Modeling and Causal Inference
  • Statistical Methods in Clinical Trials
  • Health Systems, Economic Evaluations, Quality of Life
  • Health Policy Implementation Science

Richardson has contributed to a number of recent papers, such as:

  • "Nested Markov properties for acyclic directed mixed graphs," 2023, The Annals of Statistics
  • "Estimation of local treatment effects under the binary instrumental variable model," 2021, Biometrika
  • "Experimental Design in Marketplaces," 2023, Statistical Science
  • "Foundations and new horizons for causal inference," 2020, Oberwolfach Reports
  • "An Interventionist Approach to Mediation Analysis," 2020, arXiv (Cornell University)

The scientist frequently publishes in venues including:

  • arXiv (Cornell University)
  • Biometrika
  • Journal of the Royal Statistical Society Series B (Statistical Methodology)
  • The Annals of Statistics
  • Statistical Science

Collaborations play a significant role in their research, with frequent co-authors including:

  • James M. Robins
  • Ilya Shpitser
  • James M. McQueen
  • F Richard Guo
  • Linbo Wang

Best Publications

  • Ancestral graph Markov models

    Thomas Richardson;Peter Spirtes

  • Learning high-dimensional directed acyclic graphs with latent and selection variables

    Diego Colombo;Marloes H. Maathuis;Markus Kalisch;Thomas S. Richardson

  • Chain graph models and their causal interpretations

    Steffen Lilholt Lauritzen;Thomas S. Richardson

  • The TETRAD project: Constraint based aids to causal model specification.

    Richard Scheines;Peter Spirtes;Clark Glymour;Christopher Meek

  • Demystifying Optimal Dynamic Treatment Regimes

    Erica E. M. Moodie;Thomas S. Richardson;David A. Stephens

  • An Evaluation of Machine-Learning Methods for Predicting Pneumonia Mortality

    Gregory F. Cooper;Constantin F. Aliferis;Richard Ambrosino;John M. Aronis

  • Causal inference in the presence of latent variables and selection bias

    Peter Spirtes;Christopher Meek;Thomas Richardson

  • Markov Properties for Acyclic Directed Mixed Graphs

    Thomas Richardson

  • Alternative Graphical Causal Models and the Identification of Direct E!ects

    James Robins;Thomas Richardson

  • A discovery algorithm for directed cyclic graphs

    Thomas Richardson

  • Covariate selection for the nonparametric estimation of an average treatment effect

    Xavier De Luna;Ingeborg Waernbaum;Thomas S. Richardson

  • Estimation of a covariance matrix with zeros

    Sanjay Chaudhuri;Mathias Drton;Thomas S. Richardson

  • Using path diagrams as a structural equation modeling tool

    Peter Spirtes;Thomas Richardson;Christopher Meek;Richard Scheines

  • Boosting methodology for regression problems.

    Greg Ridgeway;David Madigan;Thomas Richardson

  • An Algorithm for causal inference in the presence of latent variables and selection bias

    P. Spirtes;C. Meek;T. Richardson;Chris Meek

  • Interpretable boosted naïve Bayes classification

    Greg Ridgeway;David Madigan;Thomas Richardson;John O'Kane

  • MARKOV EQUIVALENCE FOR ANCESTRAL GRAPHS

    R. Ayesha Ali;Thomas S. Richardson;Peter Spirtes

  • Partial Identification of the Average Treatment Effect Using Instrumental Variables: Review of Methods for Binary Instruments, Treatments, and Outcomes

    Sonja A. Swanson;Sonja A. Swanson;Miguel A. Hernán;Miguel A. Hernán;Matthew Miller;Matthew Miller;James M. Robins

  • Automated discovery of linear feedback models

    Thomas Richardson;Peter Spirtes

  • Estimating Optimal Dynamic Regimes: Correcting Bias under the Null

    Erica E. M. Moodie;Thomas S. Richardson

  • Nested Markov properties for acyclic directed mixed graphs

    Thomas S. Richardson;James M. Robins;Ilya Shpitser

Frequent Co-Authors

James M. Robins
James M. Robins Harvard University
Peter Spirtes
Peter Spirtes Carnegie Mellon University
Robin J. Evans
Robin J. Evans University of Melbourne
Mathias Drton
Mathias Drton Technical University of Munich
Clark Glymour
Clark Glymour Carnegie Mellon University
David Madigan
David Madigan Northeastern University
Richard Scheines
Richard Scheines Carnegie Mellon University
Tyler J. VanderWeele
Tyler J. VanderWeele Harvard University
Steffen L. Lauritzen
Steffen L. Lauritzen University of Copenhagen
Jon Wakefield
Jon Wakefield University of Washington

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