Stable Synthetic Control with Anomaly Detection for Causal Inference
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".