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Double/Debiased/Neyman Machine Learning of Treatment Effects

Journal: American Economic Review

Date: 20170501

Author: Chernozhukov, Victor; Chetverikov, Denis; Demirer, Mert; Duflo, Esther; Hansen, Christian; Newey, Whitney

Abstract:
Chernozhukov et al. (2016) provide a generic double/de-biased machine learning (ML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using ML methods. In this note, we illustrate the application of this method in the context of estimating average treatment effects and average treatment effects on the treated using observational data.

Link: Google Scholar

Key Findings

Double Machine Learning Framework

Introduces a novel approach combining Neyman-orthogonal scores and cross-fitting to obtain valid inferential statements about treatment effects when using machine learning methods

Robust Estimation Method

Provides asymptotically unbiased and normally distributed estimates of Average Treatment Effects (ATE) and Average Treatment Effects on the Treated (ATTE)

Sample Splitting Innovation

Develops a K-fold cross-fitting procedure that reduces overfitting and enables valid inference when using complex machine learning methods

Double Machine Learning Process

  • Data is split into K equal folds for cross-fitting
  • ML methods estimate nuisance parameters on auxiliary samples
  • Treatment effects estimated using Neyman-orthogonal scores

Uncertainty Quantification

  • Standard errors account for both sampling uncertainty and partition variation
  • Median-based approach provides robustness to outliers
  • Multiple partitions enable more accurate uncertainty estimates

Estimation Framework Components

  • Integration of machine learning with classic econometric theory
  • Combines efficiency of ML with valid statistical inference
  • Maintains asymptotic efficiency while allowing flexible ML methods

Contribution and Implications

  • Bridges gap between machine learning and causal inference in economics
  • Enables reliable inference with complex ML methods in treatment effect estimation
  • Provides practical framework for empirical researchers using modern ML techniques

Data Sources

  • Flow chart based on methodology description in Section I and II of the paper
  • Uncertainty quantification visualization derived from Section III discussion
  • Components chart reflects the theoretical framework presented in Sections I-II