Time Series Treatment Effects Analysis with Always-Missing Controls

TL;DR

This paper introduces a method for estimating treatment effects in time series when the control group is unobservable, demonstrating robust results on retail sales data and providing theoretical guarantees for the estimates.

Contribution

The paper proposes a novel approach to recover unobservable control groups in time series causal inference, with proven consistency and asymptotic normality.

Findings

Accurate estimation of control group outcomes in retail sales data.

Robust prediction of holiday effects in time series.

Theoretical guarantees for treatment effect estimates.

Abstract

Estimating treatment effects in time series data presents a significant challenge, especially when the control group is always unobservable. For example, in analyzing the effects of Christmas on retail sales, we lack direct observation of what would have occurred in late December without the Christmas impact. To address this, we try to recover the control group in the event period while accounting for confounders and temporal dependencies. Experimental results on the M5 Walmart retail sales data demonstrate robust estimation of the potential outcome of the control group as well as accurate predicted holiday effect. Furthermore, we provided theoretical guarantees for the estimated treatment effect, proving its consistency and asymptotic normality. The proposed methodology is applicable not only to this always-missing control scenario but also in other conventional time series causal inference settings.