This skill covers causal machine learning methods in applied economics and quantitative social science. Use when implementing or choosing between modern ML-based causal estimators — including double machine learning, DML, partially linear models, interactive regression models, cross-fitting, Neyman orthogonality, debiased ML, causal forests, generalized random forest, GRF, honest causal trees, AIPW with machine learning, doubly robust with machine learning, DR-Learner, T-Learner, S-Learner, X-Learner, meta-learners, heterogeneous treatment effects, conditional average treatment effect, CATE, H...
At a glance
/plugin marketplace add brycewang-stanford/Auto-Empirical-Research-Skills
/plugin install causal-ml
Setup, runtime and requirements describe brycewang-stanford/Auto-Empirical-Research-Skills, the repo this skill ships in.
Also in brycewang-stanford/Auto-Empirical-Research-Skills
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