Salīdzināt metodes

	Heterogēni ārstēšanas efekti (CATE / Metamācītāji)	Algoritmi cēloņsakarību atklāšanai (PC, FCI, LiNGAM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2018	2000
Autors	Wager & Athey (causal forest); Künzel et al. (meta-learners)	Spirtes, Glymour & Scheines (PC/FCI); Shimizu et al. (LiNGAM)
Tips	Causal machine-learning framework	Causal structure learning
Pirmavots	Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association.	Spirtes, P., Glymour, C., & Scheines, R. (2000). Causation, Prediction, and Search (2nd ed.). MIT Press. ISBN: 978-0262194402
Citi nosaukumi	conditional average treatment effect, CATE, meta-learners, causal forest	PC algorithm, FCI algorithm, LiNGAM, causal structure learning
Saistītās	5	5
Kopsavilkums	Heterogeneous Treatment Effects is a machine-learning framework that estimates how a treatment effect varies across individuals — the conditional average treatment effect (CATE). It bundles meta-learner strategies such as the T-Learner, S-Learner, X-Learner and R-Learner alongside the causal forest of Wager and Athey (2018) and Künzel et al. (2019).	Causal discovery is a family of algorithms that automatically learn a directed acyclic graph (DAG) describing causal structure directly from observational data. The constraint-based PC and FCI algorithms were developed by Spirtes, Glymour and Scheines (2000), while the LiNGAM model of Shimizu et al. (2006) exploits linear non-Gaussian structure to orient edges.