Stable Synthetic Control with Anomaly Detection for Causal Inference

The study of treatment effects is an essential area in causal inference that has received extensive attention in the sciences. When access to counterfactual groups and experimental settings is limited, the synthetic control method (SCM) emerges as a key approach for observational studies. However, conventional SCM techniques mainly concentrate on addressing confounding issues in the pre-treatment period, often overlooking the confounding effects of control groups in the post-treatment period. In this paper, we propose a new approach named Stable-SC, which integrates synthetic control with anomaly detection algorithms to mitigate the influence of confounding factors in both the pre- and post-treatment periods. Our algorithm incorporates an anomaly-detection process that identifies trends and distance anomalies within control groups, significantly impacting SCM estimation results. Subsequently, we employ a re-weighting schema to adjust the significance of these abnormal groups and utilize the Difference-in-Differences estimator to assess causal effects. Through extensive experimentation with multiple simulated and real-world datasets, we demonstrate that our Stable-SC approach yields more robust estimates compared to other existing methods in the literature. Furthermore, we have successfully applied our proposed framework in diverse business scenarios within a prominent retail company, where the need for stable and robust A/B testing is paramount in quantifying causal effects.