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Ugunduzi na ML ya kisababishi

8 mbinu katika familia hii.

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Algoriti za ugunduzi wa kisababishi (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-baseAlgoriti ya FCIThe Fast Causal Inference (FCI) algorithm is a constraint-based causal discovery method introduced by Spirtes, Glymour, and Scheines in their landmark 2000 book Causation, PredictiAlgorithimu ya GESGreedy 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 iTathmini ya Athari ya Kinyume Iliyoimarishwa na Mashine ya KujifunzaMachine 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 DiscontinuityML-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 ModelThe 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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  1. Algoriti za ugunduzi wa kisababishi (PC, FCI, LiNGAM)2000na Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)
  2. Algoriti ya FCI2000na Spirtes, Glymour & Scheines
  3. Machine Learning-Augmented Marginal Structural Model2000 (MSM); 2011 (ML-augmented via targeted learning)na Robins, Hernan & Brumback (MSM, 2000); van der Laan & Rose (ML augmentation, TMLE framework, 2011)
  4. Machine Learning-Augmented Fuzzy Regression Discontinuity2001 (fuzzy RDD); 2018 (double ML augmentation)na Hahn, Todd & Van der Klaauw (fuzzy RDD); Chernozhukov et al. (ML augmentation framework)
  5. Algorithimu ya GES2002na David Maxwell Chickering
  6. Makadirio Yanayolengwa ya Uwezekano wa Juu Zaidi (TMLE)2006na Mark van der Laan & Daniel Rubin
  7. Tathmini ya Athari ya Kinyume Iliyoimarishwa na Mashine ya Kujifunza2016-2019na Chernozhukov et al.; Athey & Imbens
  8. NOTEARS: Kuendeleza Ubora kwa Kujifunza Muundo wa Kisaada2018na Zheng, Aragam, Ravikumar & Xing

Mbinu zote 8

Algoriti za ugunduzi wa kisababishi (PC, FCI, LiNGAM)Algoriti ya FCIAlgorithimu ya GESTathmini ya Athari ya Kinyume Iliyoimarishwa na Mashine ya KujifunzaMachine Learning-Augmented Fuzzy Regression DiscontinuityMachine Learning-Augmented Marginal Structural ModelNOTEARS: Kuendeleza Ubora kwa Kujifunza Muundo wa KisaadaMakadirio Yanayolengwa ya Uwezekano wa Juu Zaidi (TMLE)