GitHub - QuantLet/Outcome-adaptive-Random-Forest: Non-parametric variable selection and inference via the outcome-adaptive Random Forest (OARF). Uses the IPTW estimator to estimate the ATE while the propensity score is estimated via OARF. This leads to smaller variance and bias. Only variables that are confounders or predictive of the outcome are selected for the propensity score.

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Non-parametric variable selection and inference via the outcome-adaptive Random Forest (OARF). Uses the IPTW estimator to estimate the ATE while the propensity score is estimated via OARF. This leads to smaller variance and bias. Only variables that are confounders or predictive of the outcome are selected for the propensity score.

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Name		Name	Last commit message	Last commit date
Latest commit History 7 Commits
Empirical Examples		Empirical Examples
DGP_settings.R		DGP_settings.R
OARF_ATE_VarSelection.png		OARF_ATE_VarSelection.png
confidence_interval_coverage_Parallel.R		confidence_interval_coverage_Parallel.R
metainfo.txt		metainfo.txt
oal_ci_functions.R		oal_ci_functions.R
oarf_ci_functions.R		oarf_ci_functions.R
point_estimates_variable_coverage.R		point_estimates_variable_coverage.R