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Causal Machine Learning
Folder: Research/Econometrics/Causal-Machine-Learning
10 items under this folder.
Sep 23, 2026
Asymptotic Normality and Inference for Forests
source/ingested
topic/causal-inference
topic/machine-learning
topic/random-forests
topic/asymptotics
type/theorem
doc/paper
Sep 23, 2026
Causal Machine Learning - Overview
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
topic/semiparametric-inference
type/overview
doc/paper
Sep 23, 2026
Causal Machine Learning - Index
type/index
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
Sep 18, 2026
Cross-Fitting and Sample Splitting
source/ingested
topic/causal-inference
topic/machine-learning
topic/semiparametric-inference
type/method
doc/paper
Sep 18, 2026
DML Estimators for ATE and the Interactive Model
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
type/method
type/theorem
doc/paper
Sep 18, 2026
Generalized Random Forests - Local Moment Equations
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
topic/random-forests
type/method
doc/paper
Sep 18, 2026
Honest Trees and Causal Forests
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
topic/random-forests
type/method
doc/paper
Sep 18, 2026
Neyman Orthogonality
source/ingested
topic/causal-inference
topic/machine-learning
topic/semiparametric-inference
type/concept
type/theorem
doc/paper
Sep 18, 2026
R-Learner and Orthogonal CATE Estimation
source/ingested
topic/causal-inference
topic/machine-learning
topic/treatment-effects
type/method
doc/paper
Sep 18, 2026
Regularization Bias and the Partially Linear Model
source/ingested
topic/causal-inference
topic/machine-learning
topic/semiparametric-inference
type/concept
doc/paper