Otkrivanje i kauzalni ML
8 metoda u ovoj obitelji.
Izdvojeno
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 algoritamThe 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 algoritamGreedy 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 iStrojno učenje-augmentirana protufaktorska evaluacija utjecajaMachine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather thaProšireni neizvjesni regresijski diskontinuitet s pomoću strojnog učenjaML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. WherMarginalni strukturni model proširen strojnim učenjem (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
Put čitanja
Najreferentnije temeljne metode ove teme, poredane redoslijedom njihova razvoja — polazište ako ste ovdje novi.
Sve metode 8
Algoritmi za otkrivanje uzročnosti (PC, FCI, LiNGAM)FCI algoritamGES algoritamStrojno učenje-augmentirana protufaktorska evaluacija utjecajaProšireni neizvjesni regresijski diskontinuitet s pomoću strojnog učenjaMarginalni strukturni model proširen strojnim učenjem (ML-MSM)NOTEARS: Kontinuirana optimizacija za učenje kauzalnih strukturaCiljana procjena maksimalne vjerodostojnosti (TMLE)