Testing and interpreting the effectiveness of causal machine learning – an economic theory approach

If treatment effects vary systematically, customizing treatment status at the individual level may help increasing the overall effectiveness of an intervention. We document how the application of causal machine learning methods can successfully increase sales revenue generated within a loss framing treatment in an online field experiment. We combine this data with a behavioral experiment measuring sensitivity to loss aversion at the individual level. Our results show that treatment status as assigned by causal machine learning is consistent with treatment assignment based on economic theory.

Joint: Kevin Bauer, Andreas Grunewald, Florian Hett, Johanna Jagow, Maximilian Speicher

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