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Estimating Conditional Complier Quantile Treatment Effect via Stratified Quantile Regression.

Huijuan Ma1, Mengjiao Peng1, Jing Qin2

  • 1Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, Academy of Statistics and Interdisciplinary Sciences, East China Normal University, Shanghai, China.

Statistics in Medicine
|March 1, 2026
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Summary

This study introduces a new method to estimate causal treatment effects in randomized experiments with noncompliance, focusing on compliers. The approach enhances understanding of treatment impacts across diverse populations.

Keywords:
compliersmixture structurequantile regressiontreatment effect

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Area of Science:

  • Econometrics
  • Biostatistics
  • Causal Inference

Background:

  • Estimating causal treatment effects in randomized experiments is challenging when participants do not comply with their assigned treatment.
  • The instrumental variable (IV) framework allows estimation for 'compliers' but requires advanced methods for subgroup analysis.

Purpose of the Study:

  • To develop a novel, tuning parameter-free method for estimating the conditional complier quantile treatment effect (CQTE) based on individual characteristics.
  • To address limitations of previous methods by directly using the mixture structure in complier problems.

Main Methods:

  • Utilized stratified quantile regression models for compliers with and without treatment.
  • Introduced a new iterated algorithm to solve complex discontinuous equations.
  • Established consistency and asymptotic normality of the proposed estimators.

Main Results:

  • The proposed method effectively estimates CQTE by capturing treatment-covariate interactions.
  • Demonstrated practical utility through simulation studies and real-world data analysis (Oregon health insurance experiment, job training study).

Conclusions:

  • The new method provides a reliable and flexible approach to estimating conditional complier treatment effects.
  • Offers significant advancements in causal inference for noncompliance scenarios.