DisC2o-HD: Distributed causal inference with covariates shift for analyzing real-world high-dimensional data
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces DisC²o-HD, a distributed learning algorithm for high-dimensional healthcare data. It effectively estimates average treatment effects (ATE) while addressing covariate shift across multiple clinical sites.
Area Of Science
- Health Informatics
- Biostatistics
- Machine Learning
Background
- High-dimensional healthcare data (EHR, claims) present challenges: numerous variables, multi-site data consolidation, and covariate shift.
- Estimating treatment effects in such data requires robust methods to handle heterogeneity across sites.
Purpose Of The Study
- To propose a novel distributed learning algorithm, DisC²o-HD, for estimating the average treatment effect (ATE) in high-dimensional healthcare data.
- To address covariate shift and data heterogeneity across multiple clinical sites.
Main Methods
- Developed DisC²o-HD, a distributed learning algorithm utilizing surrogate likelihood.
- Employs propensity score and outcome model calibration to achieve covariate balancing and account for covariate shift.
- Demonstrates that the distributed estimator approximates the pooled estimator.
Main Results
- The proposed estimator is consistent if either the propensity score or outcome regression model is correctly specified.
- Achieves semiparametric efficiency when both models are correctly specified.
- Simulation studies and real-world data application validate the algorithm's performance and readiness.
Conclusions
- DisC²o-HD offers a valid and implementable solution for estimating ATE in distributed, high-dimensional healthcare data with covariate shift.
- The algorithm provides a robust approach to leveraging multi-site data while maintaining statistical validity.
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