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

Causal regularization solves several practical problems in live trading applications: estimating price impact when alpha is un- known 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