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    Machine learning foundations· 1995

    Causal diagrams for empirical research

    Judea Pearl (first author) · 1995

    Read the original

    DOI 10.1093/biomet/82.4.669OpenAlex W2049910836

    On the reading lists

    Why it matters

    Formalized graphical identification of causal effects from observational data.

    Condensed from the expert lists' notes.

    Reading it

    Difficulty: Advanced

    Read this first: probabilistic graphical models; probability.

    The record

    First released
    1995 (from OpenAlex)
    Citations
    2,338 citations, as counted by OpenAlex (CC0)
    Named by
    both expert lists
    Score
    0.460 · computed rank 278 · consensus 0.67, must-read 1, citations percentile 0.455 (1990s · foundations), awards 0, existing lists 0. Method

    Editor's notes

    • Readability check: pass: Pearl's introduction of causal diagrams and the back-door criterion. (AI draft (Claude), not yet reviewed, 2026-10-29)

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.