Time Series Counterfactual Inference with Hidden Confounders

Guangyu Li, Jiahao Chen, Samuel A Assefa, Yan Liu

Keywords: Time Series Analysis, Counterfactual Inference, Differential Equations.

Abstract: We present augmented counterfactual ordinary differential equations (ACODEs), a new approach to counterfactual inference on time series data with a focus on healthcare applications. ACODEs model interventions in continuous time with differential equations, augmented by auxiliary confounding variables to reduce inference bias. Experiments on tumor growth simulation and sepsis patient treatment response show that ACODEs outperform other methods like counterfactual Gaussian processes, recurrent marginal structural networks, and time series deconfounders in the accuracy of counterfactual inference. The learned auxiliary variables also reveal new insights into causal interventions and hidden confounders.

One-sentence Summary: This paper present a differential equation based model for time series counterfactual inference with hidden confounders.

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