因果探索・因果機械学習
8 の手法がこの系統にあります。
注目
因果発見アルゴリズム (PC, FCI, LiNGAM)Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-baseFCIアルゴリズムThe Fast Causal Inference (FCI) algorithm is a constraint-based causal discovery method introduced by Spirtes, Glymour, and Scheines in their landmark 2000 book Causation, PredictiGESアルゴリズムGreedy Equivalence Search (GES) is a score-based algorithm for learning the causal structure of a set of variables from observational data. Introduced by David Maxwell Chickering i機械学習拡張型因果影響評価Machine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather tha機械学習拡張ファジー回帰不連続デザインML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. Wher機械学習強化型周辺構造モデル（ML-MSM）The machine learning-augmented marginal structural model combines the causal rigour of Robins et al.'s MSM framework with flexible, data-adaptive ML algorithms for estimating prope
学びの道筋
このトピックで最も多く参照される基礎的な手法を、発展してきた順に並べました — はじめての方はここから読み始めてください。