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Penalized regression procedures for variable selection in the potential outcomes framework.

Debashis Ghosh1, Yeying Zhu, Donna L Coffman

  • 1Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, 80045, U.S.A.

Statistics in Medicine
|January 29, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a flexible framework for variable selection in causal inference using penalized regression. The

Keywords:
L1 penaltyaverage causal effectcounterfactualimputed datatreatment heterogeneity

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Area of Science:

  • Statistics
  • Causal Inference
  • Machine Learning

Background:

  • Model selection is a critical challenge in causal inference.
  • Penalized regression offers robust variable selection methods.
  • Integrating imputation and selection is key for complex data.

Purpose of the Study:

  • To present a unified framework for variable selection in causal inference.
  • To introduce a class of 'impute, then select' procedures.
  • To explore applications in identifying treatment effect subgroups.

Main Methods:

  • Developed a framework for penalized regression in causal inference.
  • Defined a difference least absolute shrinkage and selection operator (LASSO) algorithm.
  • Illustrated procedures with multiple imputation analogs.

Main Results:

  • The 'impute, then select' approach is agnostic to specific imputation or regression methods.
  • Model selection in causal inference is clarified as a multivariate regression problem.
  • Demonstrated the utility of the methods on a right-heart catheterization dataset.

Conclusions:

  • The proposed framework simplifies variable selection for causal effects.
  • Methods are applicable for identifying homogeneous treatment effect subgroups.
  • The approach integrates well with machine learning and missing data techniques.