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  3. Asymptotic Inference For Multi-stage Stationary Treatment Policy With Variable Selection.
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  3. Asymptotic Inference For Multi-stage Stationary Treatment Policy With Variable Selection.

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Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection.

Daiqi Gao1, Yufeng Liu2, Donglin Zeng3

  • 1Department of Statistics, Harvard University, Cambridge, MA 02138, USA.

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|March 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new method for dynamic treatment policies with high-dimensional features, improving efficiency and enabling valid statistical inference for personalized medicine.

Keywords:
Augmented inverse probability weighted estimatordynamic treatment regimehigh-dimensional inferencepolicy parametersparse estimation

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Area of Science:

  • Biostatistics
  • Machine Learning
  • Causal Inference

Background:

  • Dynamic treatment regimes tailor decisions to individual patient features over time.
  • Multi-stage stationary policies use consistent decision functions across stages based on evolving biomarkers.
  • Existing research often overlooks policy inference, particularly with high-dimensional data.

Purpose of the Study:

  • To develop a method for constructing and performing valid inference on multi-stage stationary treatment policies.
  • To address challenges posed by high-dimensional features in dynamic treatment regimes.
  • To enhance the efficiency and accuracy of policy estimation.

Main Methods:

  • Obtained multi-stage stationary treatment policies by minimizing an augmented inverse probability weighted estimator.
  • Applied an L1 penalty for feature selection in policy parameters.
  • Constructed one-step improvements for policy parameter estimators to ensure valid inference.

    • Main Results:

    • The proposed method yields a sparse policy with a near-optimal value function.
    • The improved estimators demonstrate asymptotic normality, even with high-dimensional and slowly converging nuisance parameters.
    • Numerical studies confirm the method's effectiveness in estimating policies and conducting valid inference.

    Conclusions:

    • The developed approach effectively estimates sparse dynamic treatment policies in high-dimensional settings.
    • The method provides a robust framework for valid statistical inference on treatment policies.
    • This work advances personalized medicine by enabling more accurate and efficient treatment decisions.