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

In: Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science

Author

Listed:
  • Kevin Webster
  • Nicholas Westray

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.

Suggested Citation

  • Kevin Webster & Nicholas Westray, 2025. "Getting More for Less: Better A/B Testing via Causal Regularization," World Scientific Book Chapters, in: Horst Simon (ed.), Transactions of ADIA Lab Interdisciplinary Advances in Data and Computational Science, chapter 13, pages 343-358, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789819813049_0013
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    More about this item

    Keywords

    Computational Science; Data Science; AI Applications; Climate Science; Medical Imaging; Sustainability; Interdisciplinary Research; Data Science; Mathematical and Quantitative Finance;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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