Otkrivanje i kauzalni ML
8 metoda u ovoj porodici.
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Algoritmi za otkrivanje uzročnosti (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 АлгоритамThe 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 алгоритамGreedy 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 iMašinsko učenje-augmentovana evaluacija kontrafaktuelnog uticajaMachine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather thaMašinsko učenje-augmentovani nejasni regresioni diskontinuitetni dizajnML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. WherMašinsko učenje-augmentovani marginalni strukturni 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
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Najreferentnije temeljne metode ove teme, prema redosledu njihovog nastanka — mesto za početak ako ste novi ovde.
Sve metode 8
Algoritmi za otkrivanje uzročnosti (PC, FCI, LiNGAM)FCI АлгоритамGES алгоритамMašinsko učenje-augmentovana evaluacija kontrafaktuelnog uticajaMašinsko učenje-augmentovani nejasni regresioni diskontinuitetni dizajnMašinsko učenje-augmentovani marginalni strukturni model (ML-MSM)NOTEARS: Kontinualna optimizacija za učenje kauzalne struktureCiljana procena maksimalne verodostojnosti (TMLE)