Causal Discovery
Structure learning algorithms: PC, GES, and NOTEARS.
Causal Discovery. Structure learning algorithms: PC, GES, and NOTEARS.
Foundations and canonical references
The standard treatments of causal discovery approach the subject from complementary angles. Spirtes, Causation, Prediction, and Search (2000) is the anchor reference for the subject and lays out the core definitions, theorems, and worked examples that practitioners return to.
Open methodological questions for causal discovery include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- textbook · primary · 2000Causation, Prediction, and Searchspirtes-2000, glymour-2000, scheines-2000
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