Getting more for less: better A / B testing via causal regularisation

A causal machine learning algorithm is used to estimate trades’ price impact


Causal regularisation solves several practical problems in live trading applications: estimating the price impact when alpha is unknown and estimating alpha when price impact is unknown. In addition, it increases the value of small A / B tests, allowing more robust conclusions to be drawn from live trading experiments that are smaller than those for traditional econometric methods. Trading teams, requiring less A / B test data, can run more live trading

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