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Kausale Entdeckung & kausales ML

8 Methoden in dieser Familie.

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Algorithmen zur kausalen Entdeckung (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-AlgorithmusThe 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-Algorithmus – Greedy Equivalence Search zur Kausalen EntdeckungGreedy 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 iMaschinelles Lernen-gestützte kontrafaktische WirkungsanalyseMachine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather thaMaschinelles Lernen-erweitertes Fuzzy Regression Discontinuity DesignML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. WherMaschinelles Lernen-erweitertes Marginal Structural Model (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

Leseweg

Die meistzitierten grundlegenden Methoden dieses Themas, in der Reihenfolge ihrer Entwicklung — ein Ausgangspunkt, wenn Sie hier neu sind.

  1. Algorithmen zur kausalen Entdeckung (PC, FCI, LiNGAM)2000von Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)
  2. FCI-Algorithmus2000von Spirtes, Glymour & Scheines
  3. Maschinelles Lernen-erweitertes Marginal Structural Model (ML-MSM)2000 (MSM); 2011 (ML-augmented via targeted learning)von Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011)
  4. Maschinelles Lernen-erweitertes Fuzzy Regression Discontinuity Design2001 (fuzzy RDD); 2018 (double ML augmentation)von Hahn, Todd & Van der Klaauw (fuzzy RDD); Chernozhukov et al. (ML augmentation framework)
  5. GES-Algorithmus – Greedy Equivalence Search zur Kausalen Entdeckung2002von David Maxwell Chickering
  6. Targeted Maximum Likelihood Estimation (TMLE)2006von Mark van der Laan & Daniel Rubin
  7. Maschinelles Lernen-gestützte kontrafaktische Wirkungsanalyse2016-2019von Chernozhukov et al.; Athey & Imbens
  8. NOTEARS: Kontinuierliche Optimierung für kausale Strukturlernen2018von Zheng, Aragam, Ravikumar & Xing

Alle Methoden 8

Algorithmen zur kausalen Entdeckung (PC, FCI, LiNGAM)FCI-AlgorithmusGES-Algorithmus – Greedy Equivalence Search zur Kausalen EntdeckungMaschinelles Lernen-gestützte kontrafaktische WirkungsanalyseMaschinelles Lernen-erweitertes Fuzzy Regression Discontinuity DesignMaschinelles Lernen-erweitertes Marginal Structural Model (ML-MSM)NOTEARS: Kontinuierliche Optimierung für kausale StrukturlernenTargeted Maximum Likelihood Estimation (TMLE)