World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
29591
World Ranking
5997
National Ranking
2698

Overview

Peter Spirtes is affiliated with Carnegie Mellon University in the United States. Their primary research field is Computer Science, with a focus on several subfields including Artificial Intelligence, Statistics and Probability, Management Science and Operations Research, Demography, and Information Systems and Management.

Their work covers various topics, notably Bayesian Modeling and Causal Inference, Advanced Causal Inference Techniques, Statistical Methods and Bayesian Inference, Multi-Criteria Decision Making, Scientific Computing and Data Management, Insurance, Mortality, Demography, Risk Management, and Legal Education and Practice Innovations.

Recent publications reflect ongoing contributions to causality, machine learning, and data science. Selected papers include:

  • "Causal-learn: Causal Discovery in Python," 2023, published in arXiv (Cornell University)
  • "Constructing Causal Life-Course Models: Comparative Study of Data-Driven and Theory-Driven Approaches," 2023, published in American Journal of Epidemiology
  • "Causal Discovery for Observational Sciences Using Supervised Machine Learning," 2023, published in Journal of Data Science
  • "Prompting Fairness: Integrating Causality to Debias Large Language Models," 2024, published in arXiv (Cornell University)
  • "Causal discovery and counterfactual reasoning to optimize persuasive dialogue policies," forthcoming 2025, to be published in Behaviour and Information Technology

Spirtes frequently publishes in venues such as arXiv (Cornell University), American Journal of Epidemiology, Journal of Data Science, Behaviour and Information Technology, and bioRxiv (Cold Spring Harbor Laboratory).

Collaborations with other researchers are frequent, with notable coauthors including Kun Zhang, Zeyu Tang, Xinshuai Dong, Joseph Ramsey, and Anne Petersen.

Best Publications

  • Causation, prediction, and search

    Peter Spirtes;Clark N. Glymour;Richard Scheines

  • Causation, Prediction, and Search, 2nd Edition

    Peter Spirtes;Clark Glymour;Richard Scheines

  • An Algorithm for Fast Recovery of Sparse Causal Graphs

    Peter Spirtes;Clark N. Glymour

  • Inferring causation from time series in Earth system sciences

    Jakob Runge;Jakob Runge;Sebastian Bathiany;Erik Bollt;Gustau Camps-Valls

  • Review of Causal Discovery Methods Based on Graphical Models.

    Clark Glymour;Kun Zhang;Peter Spirtes

  • Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling

    Clark Glymour;Richard Scheines;Peter Spirtes;Kevin T. Kelly

  • Ancestral graph Markov models

    Thomas Richardson;Peter Spirtes

  • Causal discovery and inference: concepts and recent methodological advances

    Peter Spirtes;Kun Zhang

  • Discovering Causal Structure.

    S. C. Pearce;C. Glymour;R. Scheines;P. Spirtes

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

    Richard Scheines;Peter Spirtes;Clark Glymour;Christopher Meek

  • Causal inference

    Unknown

  • 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

  • Learning Bayesian networks with discrete variables from data

    Peter Spirtes;Christopher Meek

  • Adjacency-faithfulness and conservative causal inference

    Joseph Ramsey;Peter Spirtes;Jiji Zhang

  • Introduction to Causal Inference

    Peter Spirtes

  • Learning the Structure of Linear Latent Variable Models

    Ricardo Silva;Richard Scheines;Clark Glymour;Peter Spirtes

  • Directed cyclic graphical representations of feedback models

    Peter Spirtes

  • Uniform consistency in causal inference

    James M. Robins;Richard Scheines;Peter Spirtes;Larry Wasserman

  • Causality From Probability

    Peter Spirtes;Clark N. Glymour;Richard Scheines

  • Using path diagrams as a structural equation modeling tool

    Peter Spirtes;Thomas Richardson;Christopher Meek;Richard Scheines

  • Discovering cyclic causal models by independent components analysis

    Gustavo Lacerda;Peter Spirtes;Joseph Ramsey;Patrik O. Hoyer

Frequent Co-Authors

Clark Glymour
Clark Glymour Carnegie Mellon University
Richard Scheines
Richard Scheines Carnegie Mellon University
Thomas S. Richardson
Thomas S. Richardson University of Washington
Gregory F. Cooper
Gregory F. Cooper University of Pittsburgh
Kun Zhang
Kun Zhang Carnegie Mellon University
Patrik O. Hoyer
Patrik O. Hoyer University of Helsinki
Larry Wasserman
Larry Wasserman Carnegie Mellon University
Constantin F. Aliferis
Constantin F. Aliferis University of Minnesota
Jakob Zscheischler
Jakob Zscheischler Helmholtz Centre for Environmental Research
Dim Coumou
Dim Coumou Vrije Universiteit Amsterdam

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