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Disentangled representation for sequential treatment effect estimation.

Jiebin Chu1, Yaoyun Zhang2, Fei Huang2

  • 1Zhejiang University, Hangzhou, Zhejiang Province, China.

Computer Methods and Programs in Biomedicine
|October 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel disentangled representation learning method to accurately estimate dynamic treatment effects from longitudinal data. The approach effectively handles time-varying confounders, outperforming existing models in prediction accuracy.

Keywords:
Causal inferenceDeep learningRepresentation learningSequential dataTreatment effect estimation

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Area of Science:

  • Causal Inference
  • Machine Learning
  • Biostatistics

Background:

  • Estimating treatment effects is crucial but challenged by confounders in observational data.
  • Time-varying confounders in longitudinal data complicate accurate treatment effect estimation.
  • Existing methods struggle with time-varying confounders in sequential data.

Purpose of the Study:

  • To develop a novel method for dynamic treatment effect estimation using longitudinal observational data.
  • To address the challenge of time-varying confounders in treatment effect estimation.
  • To improve the accuracy of treatment effect estimation in complex longitudinal settings.

Main Methods:

  • Employs disentangled representation learning to decompose data into outcome, treatment, and confounder factors.
  • Utilizes a recurrent neural network framework to process sequential information.
  • Incorporates mutual information-based regularization to eliminate time-varying confounders and reduce selection bias.

Main Results:

  • The proposed model demonstrates superior performance in one-step and five-step ahead predictions compared to state-of-the-art models.
  • Achieved lower normalized root mean square error (0.70% vs 0.74% for one-step, 1.47% vs 1.83% for five-step).
  • Visualization confirms the model's effectiveness in disentangling representations and handling confounders.

Conclusions:

  • The model effectively learns disentangled representations from longitudinal data.
  • It successfully addresses the challenge of time-varying confounders in dynamic treatment effect estimation.
  • The proposed method achieves superior performance in estimating dynamic treatment effects.