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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Recursive partitioning for heterogeneous causal effects.

Susan Athey1, Guido Imbens2

  • 1Stanford Graduate School of Business, Stanford University, Stanford, CA 94305 athey@stanford.edu.

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|July 7, 2016
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Summary
This summary is machine-generated.

This study introduces methods for estimating causal effect heterogeneity in studies. Honest estimation improves confidence interval coverage for treatment effects across population subsets.

Keywords:
causal inferencecross-validationheterogeneous treatment effectspotential outcomessupervised machine learning

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Area of Science:

  • Causal inference
  • Statistical modeling
  • Machine learning

Background:

  • Estimating causal effects is crucial in experimental and observational studies.
  • Understanding heterogeneity in treatment effects across different population subsets is a key challenge.
  • Existing methods may struggle with high dimensionality and require strong assumptions.

Purpose of the Study:

  • To propose methods for estimating heterogeneity in causal effects.
  • To develop data-driven approaches for partitioning populations based on treatment effects.
  • To enable valid confidence intervals for treatment effects, even with many covariates.

Main Methods:

  • Utilizing regression tree methods adapted for treatment effect estimation.
  • Implementing an "honest" estimation approach using separate data samples for partitioning and effect estimation.
  • Developing a model selection criterion that accounts for bias elimination and variance in subpopulations.

Main Results:

  • Honest estimation achieved nominal coverage for 90% confidence intervals in simulations.
  • Non-honest approaches resulted in significantly lower coverage (74%-84%).
  • The cost in mean squared error for honest estimation ranged from 7-22%.

Conclusions:

  • The proposed "honest" estimation method enhances the reliability of causal effect heterogeneity analysis.
  • This approach provides valid confidence intervals without sparsity assumptions.
  • The methods are robust and improve upon standard techniques in simulation studies.