인과 발견 및 인과 머신러닝
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인과관계 발견 알고리즘 (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 알고리즘The 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 알고리즘Greedy 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 i기계 학습 증강 반사실적 영향 평가Machine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather tha기계 학습 증강 퍼지 회귀 불연속 설계ML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. Wher기계 학습 증강 주변 구조 모델 (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
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