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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Propensity Weighted federated learning for treatment effect estimation in distributed imbalanced environments.

Alejandro Almodóvar1, Juan Parras1, Santiago Zazo1

  • 1Information Processing and Telecommunication Center, ETSI de Telecomunicación, Universidad Politécnica de Madrid, Spain.

Computers in Biology and Medicine
|June 29, 2024
PubMed
Summary

Estimating treatment effects across hospitals with varying patient data and privacy rules is challenging. A new federated learning method, Propensity Weighted Federated Averaging (PW FedAvg), improves causal inference accuracy even with imbalanced treatment distributions.

Keywords:
Causal inferenceCounterfactual predictionFederated learningPropensity scoreTreatment effects

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Area of Science:

  • Medical Informatics
  • Causal Inference
  • Machine Learning

Background:

  • Estimating treatment effects from observational data is crucial in medicine due to data availability and the limitations of randomized controlled trials.
  • Challenges arise in distributed causal inference, particularly when patient data cannot be shared between hospitals with differing patient distributions and treatment assignment criteria.
  • Existing federated learning approaches may struggle with significant shifts in treatment distributions across decentralized nodes.

Purpose of the Study:

  • To address the challenge of distributed causal inference in healthcare settings with privacy constraints and heterogeneous treatment distributions.
  • To propose a novel federated learning algorithm adapted for estimating individual treatment effects in decentralized environments.
  • To evaluate the performance of the proposed algorithm against existing methods under varying degrees of treatment distribution imbalance.

Main Methods:

  • Adaptation of the FederatedAveraging algorithm to the Treatment Effect DEcision VAult Estimation (TEDVAE) neural network model.
  • Development of Propensity Weighted Federated Averaging (PW FedAvg) to account for shifts in treatment assignment distributions across hospitals.
  • Experimental comparison of PW FedAvg with Vanilla Federated Averaging and other federated learning causal inference algorithms.

Main Results:

  • PW FedAvg demonstrates improved accuracy in estimating individual causal effects compared to baseline methods, especially when treatment distributions are highly imbalanced across hospitals.
  • The proposed method effectively handles variations in patient populations and treatment assignment criteria without direct data sharing.
  • Experimental results show significant error reduction in causal effect estimation with PW FedAvg under challenging imbalance scenarios.

Conclusions:

  • PW FedAvg offers a robust solution for privacy-preserving distributed causal inference in healthcare.
  • The algorithm's ability to manage treatment distribution shifts makes it suitable for real-world clinical settings with decentralized data.
  • This work advances the application of federated learning for reliable treatment effect estimation in complex observational data scenarios.