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SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes
Zhaozhi Qian · Yao Zhang · Ioana Bica · Angela Wood · Mihaela van der Schaar

Tue Dec 07 08:30 AM -- 10:00 AM (PST) @ Virtual

Most of the medical observational studies estimate the causal treatment effects using electronic health records (EHR), where a patient's covariates and outcomes are both observed longitudinally. However, previous methods focus only on adjusting for the covariates while neglecting the temporal structure in the outcomes. To bridge the gap, this paper develops a new method, SyncTwin, that learns a patient-specific time-constant representation from the pre-treatment observations. SyncTwin issues counterfactual prediction of a target patient by constructing a synthetic twin that closely matches the target in representation. The reliability of the estimated treatment effect can be assessed by comparing the observed and synthetic pre-treatment outcomes. The medical experts can interpret the estimate by examining the most important contributing individuals to the synthetic twin. In the real-data experiment, SyncTwin successfully reproduced the findings of a randomized controlled clinical trial using observational data, which demonstrates its usability in the complex real-world EHR.

Author Information

Zhaozhi Qian (University of Cambridge)
Yao Zhang (University of Cambridge)
Ioana Bica (University of Oxford)
Angela Wood (University of Cambridge)
Mihaela van der Schaar (University of Cambridge)

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