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Treatment effect estimators for count data models.

Takuya Hasebe1

  • 1Faculty of Liberal Arts, Sophia University, Tokyo, Japan.

Health Economics
|June 30, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a switching regression model for count data, accounting for endogenous state selection and lognormal heterogeneity. It estimates multiple treatment effects, including average treatment effect, to analyze public insurance impacts on healthcare utilization.

Keywords:
count datadoctor visitslognormalpublic insurancetreatment effects

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Area of Science:

  • Econometrics
  • Biostatistics
  • Health Economics

Background:

  • Switching regression models are crucial for analyzing outcomes influenced by unobserved state selection.
  • Count data models are essential for healthcare utilization, such as doctor visits.
  • Endogenous treatment selection complicates accurate estimation of treatment effects.

Purpose of the Study:

  • To develop and apply a switching regression model for count data with endogenous state selection.
  • To derive estimators for various treatment effects within this framework.
  • To examine the impact of public insurance on the number of doctor visits.

Main Methods:

  • Utilizing a switching regression model adapted for count data outcomes.
  • Assuming lognormal latent heterogeneity to model unobserved factors.
  • Deriving estimators for average treatment effect (ATE), ATE on the treated (ATT), local average treatment effect (LATE), and marginal treatment effect (MTE).

Main Results:

  • The model successfully estimates diverse treatment effects in the presence of endogenous selection.
  • The application demonstrates the model's capability in analyzing real-world healthcare data.
  • Quantified effects of public insurance on doctor visits provide valuable insights.

Conclusions:

  • The proposed switching regression model offers a robust framework for count data with endogenous selection.
  • Accurate estimation of treatment effects is achievable even with unobserved heterogeneity.
  • Findings contribute to understanding healthcare policy impacts on patient behavior.