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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 of Quantlet: Outcome-adaptive-Random-Forest

Published in: Variable Selection for Causal Inference via Outcome-adaptive Random Forest (IRTG Discussion Paper)

Description: Replication code for estimating the ATE via inverse probability weighting.

Keywords: IPTW, ATE, variable-selection, Random-Forest

Author: Daniel Jacob

See also: https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.12679

Submitted: 26.07.2021
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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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random-forest adaptive-learning ate causal-inference inverse-probability-weights

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