Getting More for Less: Better A/B Testing via Causal Regularization

Kevin Webster and Nicholas Westray

Chapter 13 in Transactions of ADIA Lab:Interdisciplinary Advances in Data and Computational Science, 2025, pp 343-358 from World Scientific Publishing Co. Pte. Ltd.

Abstract: Causal regularization solves several practical problems in live trading applications: estimating price impact when alpha is unknown and estimating alpha when price impact is unknown. In addition, causal regularization increases the value of small A/B tests: one draws more robust conclusions from smaller live trading experiments than traditional econometric methods. Requiring less A/B test data, trading teams can run more live trading experiments and improve the performance of more trading algorithms. Using a realistic order simulator, we quantify these benefits for a canonical A/B trading experiment.

Keywords: Computational Science; Data Science; AI Applications; Climate Science; Medical Imaging; Sustainability; Interdisciplinary Research; Data Science; Mathematical and Quantitative Finance (search for similar items in EconPapers)
JEL-codes: C45 C63 G11 Q54 (search for similar items in EconPapers)
Date: 2025
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