Source
DoubleML — Bach, Chernozhukov, Kurz & Spindler
@misc{doubleml,
title = {DoubleML},
author = {Bach and Chernozhukov and Kurz and Spindler},
howpublished = {\url{https://docs.doubleml.org/}},
note = {Software / documentation}
}Summary by StatsDoge
Neyman-orthogonal, cross-fitted estimation of treatment effects with arbitrary ML nuisance learners.
You're looking at a building block — one of the estimators a
workflow uses inside its pipeline.
You reached it from a workflow step; it's used in 1 workflow (listed below).
https://github.com/DoubleML/doubleml-for-py
unpinned — link may rot
⚠️ Unofficial community write-up of DoubleML. This account is not affiliated with the authors; it summarizes the public documentation for demonstration. All credit & copyright belong to the original authors.
What it does
Estimates causal effects with Neyman-orthogonal scores and cross-fitting, so you can plug in any ML learner (random forest, lasso, boosting) for the nuisance functions without contaminating the effect estimate.
import doubleml as dml
dml_plr = dml.DoubleMLPLR(dml_data, ml_l, ml_m)
dml_plr.fit().summary
Used in these workflows (1)
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Double machine learning for the 401(k) effect (DoubleML)
Effect of 401(k) eligibility on net assets via PLR / IRM / IIVM with cross-fit ML nuisances — four learners, one honest comparison.
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