World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
29591
World Ranking
5998
National Ranking
2699

Peter Spirtes publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Peter Spirtes sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 167 publications — 33rd percentile

33% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Peter Spirtes D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Peter Spirtes sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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