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Related Experiment Video

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On learning disentangled representations for individual treatment effect estimation.

Jiebin Chu1, Zhoujian Sun1, Wei Dong2

  • 1Zhejiang University, Hangzhou, China.

Journal of Biomedical Informatics
|November 3, 2021
PubMed
Summary
This summary is machine-generated.

Estimating individualized treatment effects (ITE) from observational data is challenging due to selection bias. This study introduces a novel representation learning model that disentangles latent factors to accurately estimate ITE by reducing selection bias.

Keywords:
Auxiliary-task learningCausal inferenceDeep learningDisentangled representationIndividualized treatment effectObservational data

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Area of Science:

  • Machine Learning
  • Causal Inference
  • Observational Data Analysis

Background:

  • Estimating individualized treatment effects (ITE) from observational data is complex due to selection bias.
  • Selection bias arises from discrepancies between treatment groups caused by feature-treatment assignment dependence.
  • Latent factors influencing features are hypothesized as outcome-specific, treatment-specific, and confounders.

Purpose of the Study:

  • To mitigate selection bias in ITE estimation from observational data.
  • To reduce the influence of treatment-related factors (treatment-specific factors and confounders) on outcome prediction.
  • To develop a novel representation learning model for precise ITE estimation.

Main Methods:

  • A novel representation learning model is proposed, utilizing both outcome prediction and treatment assignment classification tasks.
  • Learned representations are outcome-oriented and treatment-oriented.
  • Individualized orthogonal regularization is incorporated to disentangle confounders by making representations orthogonal.

Main Results:

  • The proposed model was evaluated on semi-simulated and real-world datasets.
  • Experimental results demonstrated competitive or superior performance compared to state-of-the-art models.
  • The method effectively reduces selection bias in ITE estimation.

Conclusions:

  • The proposed method effectively reduces selection bias in ITE estimation.
  • Disentangled representation learning through auxiliary tasks and orthogonal regularization improves ITE accuracy.
  • This approach offers a novel solution for robust ITE estimation from observational data.