Kausal oppdagelse og kausal maskinlæring
8 metoder i denne familien.
Utvalgte
Algoritmer for kausal oppdagelse (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-algoritmenGreedy 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 iMaskinlæringsforsterket kontrafaktisk effektvurderingMachine learning-augmented counterfactual impact evaluation combines the credibility of potential-outcomes causal inference with the flexibility of modern ML algorithms. Rather thaMaskinlæringsforsterket Fuzz-regresjonsdiskontinuitetsdesignML-augmented fuzzy RDD extends the classical fuzzy regression discontinuity design by replacing parametric polynomial approximations with flexible machine learning estimators. WherMaskinlæringsforsterket marginal strukturell modell (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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Algoritmer for kausal oppdagelse (PC, FCI, LiNGAM)FCI-algoritmenGES-algoritmenMaskinlæringsforsterket kontrafaktisk effektvurderingMaskinlæringsforsterket Fuzz-regresjonsdiskontinuitetsdesignMaskinlæringsforsterket marginal strukturell modell (ML-MSM)NOTEARS: Kontinuerlig optimering for læring av kausal strukturTargeted Maximum Likelihood Estimation (TMLE)