Double machine learning and automated confounder selection: A cautionary tale

Double machine learning and automated confounder selection: A cautionary tale

Double machine learning (DML) has become an increasingly popular tool for automated variable selection in high-dimensional settings. Even though the ability to deal with a large number of potential covariates can render selection-on-observables assumptions more plausible, there is at the same time a...

Full description

Bibliographic Details
Main Authors: Hünermund Paul, Louw Beyers, Caspi Itamar
Format: Article
Language:English
Published: De Gruyter 2023-05-01
Series:Journal of Causal Inference
Subjects:
double/debiased machine learning
bad controls
backdoor adjustment
collider bias
causal hierarchy
62d20
Online Access:https://doi.org/10.1515/jci-2022-0078

Internet

https://doi.org/10.1515/jci-2022-0078

Similar Items