Proximal Causal Inference for Contemporaneous Treatment Effect Estimation in Time Series Data

Music City Center 

Unmeasured confounding challenges causal inference in intensive longitudinal studies, potentially biasing treatment effect estimates. The proximal causal learning framework offers a promising approach to nonparametric identification using proxies or negative control variables in the presence of hidden confounding bias. While prior literature considers the joint effect of time-varying treatments, our work extends the framework to a time series setting to estimate the contemporaneous effect of time-varying treatments. We demonstrate that under traditional proximal causal inference assumptions, we can recover unbiased effect estimation in the presence of unmeasured confounding by leveraging the intensive longitudinal nature of time series data. Specifically, we use past and future observations as natural proxies for unmeasured confounders and revise the bridge function for valid estimation. Simulation studies illustrate our method's potential for studying environmental science and wearable device data. Our work contributes to the growing literature on proximal causal inference and provides a powerful tool for analyzing longitudinal data, including in mobile health research.

unmeasured confounding

proximal causal inference

time-series

mhealth 

Biometrics Section