Kausal opdagelse og kausal maskinlæring
8 metoder i denne familie.
Udvalgte
Algoritmer til kausal opdagelse (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 AlgoritmenThe 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 iMaskinlærings-augmenteret kontrafaktisk effektvurderingMachine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather thaMaskinlærings-augmenteret fuzzy RDD (Regression Discontinuity Design)ML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. WherMaskinlærings-augmenteret marginal strukturel 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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Alle metoder 8
Algoritmer til kausal opdagelse (PC, FCI, LiNGAM)FCI AlgoritmenGES AlgoritmeMaskinlærings-augmenteret kontrafaktisk effektvurderingMaskinlærings-augmenteret fuzzy RDD (Regression Discontinuity Design)Maskinlærings-augmenteret marginal strukturel model (ML-MSM)NOTEARS: Kontinuerlig optimering til læring af kausal strukturMålrettet Maksimum Likelihood Estimation (TMLE)