Time-Aware Synthetic Control

TL;DR

Time-Aware Synthetic Control (TASC) enhances causal inference in time-series data by incorporating temporal structure through a state-space model, Kalman filtering, and smoothing, outperforming traditional methods especially with strong trends and noisy data.

Contribution

TASC introduces a novel approach using a state-space model with a constant trend and low-rank structure, improving counterfactual inference in temporal data.

Findings

TASC outperforms existing methods in simulated datasets with strong trends.

TASC provides more accurate policy evaluation in real-world applications.

TASC is robust to high observation noise.

Abstract

The synthetic control (SC) framework is widely used for observational causal inference with time-series panel data. SC has been successful in diverse applications, but existing methods typically treat the ordering of pre-intervention time indices interchangeable. This invariance means they may not fully take advantage of temporal structure when strong trends are present. We propose Time-Aware Synthetic Control (TASC), which employs a state-space model with a constant trend while preserving a low-rank structure of the signal. TASC uses the Kalman filter and Rauch-Tung-Striebel smoother: it first fits a generative time-series model with expectation-maximization and then performs counterfactual inference. We evaluate TASC on both simulated and real-world datasets, including policy evaluation and sports prediction. Our results suggest that TASC offers advantages in settings with strong temporal trends and high levels of observation noise.