Advancing Causal Inference: Differences-in-Differences vs. Bayesian Structural Time Series Models

Abstract

As competition becomes fierce and competitive environments change more dynamically than ever, understanding the implications of any strategic decision has become important for firms to maximize the effect of their resource allocation. Thus, evaluating the causal inference of strategy implementation is critical for strategists, yet few studies address practitioners’ needs in this manner. While a few studies have prioritized the need for causal inference by employing the differences-in-differences (DD) method in management research, the inherit limitations of this method pose problems for the suitability and reliability of its results. Thus, this study aims to introduce a new approach for causal inference in management research, the Bayesian structural time series (BSTS) model. The BSTS analysis enables us to investigate causal inference from existing whether or not to when and how long. Using customer data from a Korean e-commerce platform, we show why BSTS can be superior to DD thereby providing a basis for subsequent use of causal inference studies delivering practical management implications.