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Ontdekking & causale ML

8 methoden in deze familie.

Uitgelicht

Causale Ontdekking Algoritmen (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 AlgoritmeThe 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 AlgoritmeGreedy 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 iMachine Learning-Aangevulde Contrafeitelijke ImpactevaluatieMachine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather thaMachine Learning-Augmented Fuzzy Regression Discontinuity DesignML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. WherMachine Learning-Augmented 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

Leesroute

De meest geraadpleegde fundamentele methoden van dit onderwerp, in de volgorde waarin ze zijn ontwikkeld — een plek om te beginnen als u hier nieuw bent.

  1. Causale Ontdekking Algoritmen (PC, FCI, LiNGAM)2000door Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)
  2. FCI Algoritme2000door Spirtes, Glymour & Scheines
  3. Machine Learning-Augmented Marginal Structural Model (ML-MSM)2000 (MSM); 2011 (ML-augmented via targeted learning)door Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011)
  4. Machine Learning-Augmented Fuzzy Regression Discontinuity Design2001 (fuzzy RDD); 2018 (double ML augmentation)door Hahn, Todd & Van der Klaauw (fuzzy RDD); Chernozhukov et al. (ML augmentation framework)
  5. GES Algoritme2002door David Maxwell Chickering
  6. Targeted Maximum Likelihood Estimation (TMLE)2006door Mark van der Laan & Daniel Rubin
  7. Machine Learning-Aangevulde Contrafeitelijke Impactevaluatie2016-2019door Chernozhukov et al.; Athey & Imbens
  8. NOTEARS: Continue Optimalisatie voor Causale Structuurlering2018door Zheng, Aragam, Ravikumar & Xing

Alle methoden 8

Causale Ontdekking Algoritmen (PC, FCI, LiNGAM)FCI AlgoritmeGES AlgoritmeMachine Learning-Aangevulde Contrafeitelijke ImpactevaluatieMachine Learning-Augmented Fuzzy Regression Discontinuity DesignMachine Learning-Augmented Marginal Structural Model (ML-MSM)NOTEARS: Continue Optimalisatie voor Causale StructuurleringTargeted Maximum Likelihood Estimation (TMLE)