Primary Submission Category: Design-Based Causal Inference
Unifying regression-based and design-based causal inference in time-series experiments
Authors: Zhexiao Lin, Peng Ding,
Presenting Author: Zhexiao Lin*
Time-series experiments, also called switchback experiments, play increasingly important roles in modern applications and are a fundamental experimental design in practice. In this paper, we examine the design-based properties of regression-based methods for estimating treatment effects in such settings. We demonstrate that the treatment effect of interest can be consistently estimated using ordinary least squares (OLS) with an appropriately specified working model. Our analysis extends to estimating a diverging number of treatment effects simultaneously, and we establish the asymptotic properties of the resulting estimators. Additionally, we show that the heteroskedasticity and autocorrelation consistent (HAC) estimator provides a conservative estimate of the variance. Importantly, while our approach relies on OLS regression, our theoretical framework accommodates misspecification of the regression model.