SyncTwin: Transparent Treatment Effect Estimation under Temporal Confounding

Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela Wood, Mihaela van der Schaar

Keywords: treatment effect, interpretability, healthcare, causal inference

Abstract: Estimating causal treatment effects using observational data is a problem with few solutions when the confounder has a temporal structure, e.g. the history of disease progression might impact both treatment decisions and clinical outcomes. For such a challenging problem, it is desirable for the method to be transparent --- the ability to pinpoint a small subset of data points that contributes most to the estimate and to clearly indicate whether the estimate is reliable or not. This paper develops a new method, SyncTwin, to overcome temporal confounding in a transparent way. SyncTwin estimates the treatment effect of a target individual by comparing the outcome with its synthetic twin, which is constructed to closely match the target in the representation of the temporal confounders. SyncTwin achieves transparency by enforcing the synthetic twin to only depend on the weighted combination of few other individuals in the dataset. Moreover, the quality of the synthetic twin can be assessed by a performance metric, which also indicates the reliability of the estimated treatment effect. Experiments demonstrate that SyncTwin outperforms the benchmarks in clinical observational studies while still being transparent.

One-sentence Summary: We develop SyncTwin, a transparent treatment effect estimation method that deals with confounders with temporal structures and has a broad range of applications in clinical observational studies and beyond.

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