Causal Representation

Causal Representation is a formal modeling framework that specifies the causal dependencies among variables in a system, distinguishing itself from associative or statistical representations by encoding directed influence relationships grounded in interventionist or counterfactual semantics.

361

Publications

21.7K

Citations

967

Authors

418

Institutions

Publications per year

2017–2026

146

Authors

967

  1. BS
    Bernhard Schölkopf

    Max Planck Institute for Intelligent Systems, Germany

    6 publications · 1.2K citations

    6

    h-index

  2. JB
    Joshua B. Tenenbaum

    Massachusetts Institute of Technology, United States

    5 publications · 182 citations

    5

    h-index

  3. KZ
    Kun Zhang

    Carnegie Mellon University, United States

    5 publications · 260 citations

    5

    h-index

  4. BR
    Bob Rehder

    New York University, United States

    5 publications · 381 citations

    5

    h-index

  5. WJ
    William J. Torres Bobadilla

    Max Planck Institute for Physics, Germany

    5 publications · 158 citations

    5

    h-index

1–5 of 967

Venues

  1. arXiv (Cornell University)

    Repository · 32 publications · 920 citations

    18

    h-index

  2. Cognitive Science

    15 publications · 1K citations

    14

    h-index

  3. eScholarship (California Digital Library)

    Repository · 8 publications · 120 citations

    7

    h-index

  4. Psychological Science

    6 publications · 994 citations

    6

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 280 citations

    5

    h-index

1–5