Double Machine Learning based Program Evaluation under Unconfoundedness

Abstract

This paper reviews, applies and extends recently proposed methods based on Double Machine Learning (DML) with a focus on program evaluation under unconfoundedness. DML based methods leverage flexible prediction models to adjust for confounding variables in the estimation of (i) standard average effects, (ii) different forms of heterogeneous effects, and (iii) optimal treatment assignment rules. An evaluation of multiple programs of the Swiss Active Labour Market Policy illustrates how DML based methods enable a comprehensive program 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.

References in corpus (6)

• Comment: Performance of Double-Robust Estimators When ``Inverse Probability'' Weights Are Highly Variable
• Doubly Robust Policy Evaluation and Learning
• Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
• A unifying approach for doubly-robust ${\ell }_1$ regularized estimation of causal contrasts
• Honest data-adaptive inference for the average treatment effect under model misspecification using penalised bias-reduced double-robust estimation
• Estimation and Inference with Trees and Forests in High Dimensions

Cited by in corpus (9)

• DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R
• Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena
• Distributed Double Machine Learning with a Serverless Architecture
• DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python
• Analysing a built-in advantage in asymmetric darts contests using causal machine learning
• Regularizing Double Machine Learning in Partially Linear Endogenous Models
• From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework
• Double/Debiased Machine Learning for Logistic Partially Linear Model
• Active labour market policies for the long-term unemployed: New evidence from causal machine learning