🤖 AI Summary
This study addresses causal inference in time series settings with latent confounding and staggered interventions, where conventional methods are constrained by explicit temporal assumptions or convex combination requirements. The authors propose a deep representation learning framework that jointly learns low-dimensional individual latent representations and propensity scores, enabling estimation of individual counterfactual outcomes through a flexible nonparametric matching mechanism—without presupposing a specific temporal dynamics structure. By circumventing the convexity constraints inherent in synthetic control methods, the approach effectively mitigates bias arising from unobserved confounders. Empirical evaluations on both electricity demand response and ICU clinical datasets demonstrate that the proposed method significantly improves the accuracy of counterfactual prediction and average treatment effect estimation in nonstationary dynamic environments.
📝 Abstract
Accurately estimating treatment effects in time series is essential for evaluating interventions in real-world applications, especially when treatment assignment is biased by unobserved factors. In many practical settings, interventions are adopted at different times across individuals, leading to staggered treatment exposure and heterogeneous pre-treatment histories. In such cases, aggregating outcome trajectories across treated units is ill-defined, making individual treatment effect (ITE) estimation a prerequisite for reliable causal inference. We therefore study the problem of estimating the average treatment effect for the treated (ATT) by first recovering individual-level counterfactuals. We introduce a neural framework that learns simultaneously low-dimensional latent representations of individual time series and propensity scores. These estimates are then used to approximate the individual treatment effects through a flexible matching procedure that avoids classical convexity constraints commonly used in synthetic control methods. By operating at the individual level, our approach naturally accommodates staggered interventions and improves counterfactual estimation under latent bias, without relying on explicit temporal modeling assumptions. We illustrate our approach on both real-world energy consumption data and clinical time series, including high-frequency electricity demand-response programs and semi-synthetic data for individuals in intensive care unit (ICU), where hidden confounding, staggered treatment adoption, and non-stationary dynamics are prevalent.