SPORTSCausal: Spill-Over Time Series Causal Inference

TL;DR

SPORTSCausal introduces a new method for causal inference in time series data that overcomes limitations of traditional RCT assumptions, enabling robust treatment effect estimation in real-world scenarios with spillover effects.

Contribution

The paper presents SPORTSCausal, a novel approach for causal inference in time series that does not rely on strong assumptions like independence, demonstrated through a real-world experiment.

Findings

SPORTSCausal provides consistent treatment effect estimates despite spillover effects.

Traditional methods like ANCOVA fail under spillover conditions, unlike SPORTSCausal.

The method is validated with a real-world budget-control experiment.

Abstract

Randomized controlled trials (RCTs) have long been the gold standard for causal inference across various fields, including business analysis, economic studies, sociology, clinical research, and network learning. The primary advantage of RCTs over observational studies lies in their ability to significantly reduce noise from individual variance. However, RCTs depend on strong assumptions, such as group independence, time independence, and group randomness, which are not always feasible in real-world applications. Traditional inferential methods, including analysis of covariance (ANCOVA), often fail when these assumptions do not hold. In this paper, we propose a novel approach named \textbf{Sp}ill\textbf{o}ve\textbf{r} \textbf{T}ime \textbf{S}eries \textbf{Causal} (\verb+SPORTSCausal+), which enables the estimation of treatment effects without relying on these stringent assumptions. We demonstrate the practical applicability of \verb+SPORTSCausal+ through a real-world budget-control experiment. In this experiment, data was collected from both a 5\% live experiment and a 50\% live experiment using the same treatment. Due to the spillover effect, the vanilla estimation of the treatment effect was not robust across different treatment sizes, whereas \verb+SPORTSCausal+ provided a robust estimation.