.png){kind=icon}
Return
Double machine learning-based programme evaluation under unconfoundedness
.png){kind=icon}
OA
AI
DOI：10.1093/ectj/utac015.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper reviews, applies, and extends recently proposed methods based on double machine learning (DML) with a focus on programme evaluation under unconfoundedness. DML-based methods leverage flexible prediction models to adjust for confounding variables in the estimation of (a) standard average effects, (b) different forms of heterogeneous effects, and (c) optimal treatment assignment rules. An evaluation of multiple programmes of the Swiss Active Labour Market Policy illustrates how DML-based methods enable a comprehensive programme evaluation. Motivated by extreme individualised treatment effect estimates of the DR-learner, we propose the normalised DR-learner (NDR-learner) to address this issue. The NDR-learner acknowledges that individualised effect estimates can be stabilised by an individualised normalisation of inverse probability weights.
Keywords:
Causal machine learning
conditional average treatment effects
DR-learner
individualised treatment rules
multiple treatments
policy learning
Journal
IF:
7
Papers:
565
Citations:
2.3K
Organization
No organization information available
Cited Papers
ShareSaveWho Should Be Treated? Empirical Welfare Maximization Methods for Treatment Choice.png)
ECONOMETRICA
OAAI
ECONOMETRICAIF7.1
OAAIShareSaveShareSaveShareSaveShareSaveA Simple Method for Estimating Interactions Between a Treatment and a Large Number of CovariatesOAAI
OAAIShareSave
View more