Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation.

Area of Science:

• Healthcare Analytics
• Causal Inference
• Federated Learning

Background:

• Individual Treatment Effect (ITE) estimation is vital for personalized healthcare but challenged by data privacy.
• Centralized ITE methods are impractical due to data sharing restrictions across hospitals.
• Decentralized data in federated settings introduces confounding bias, complicating accurate ITE estimation.

Purpose of the Study:

• To develop a federated learning approach for accurate ITE estimation without raw data sharing.
• To address confounding bias inherent in decentralized healthcare datasets.
• To enable personalized treatment strategy design in clinical settings.

Main Methods:

• Proposed FED-IPTW, a novel federated algorithm extending Inverse Probability Treatment Weighting (IPTW).
• Ensured global and local decorrelation between covariates and treatments within the federated framework.
• Validated on mechanical ventilation treatment effects for ICU patients with breathing difficulties.

Main Results:

• FED-IPTW demonstrated superior performance compared to state-of-the-art methods.
• Achieved high accuracy in factual prediction and ITE estimation tasks.
• Outperformed existing approaches on both synthetic and real-world eICU datasets.

Conclusions:

• FED-IPTW effectively enables privacy-preserving ITE estimation in federated healthcare settings.
• The method mitigates confounding bias, crucial for reliable causal effect analysis.
• Paves the way for advanced personalized treatment strategies, particularly in critical care like mechanical ventilation.