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Causal machine learning methods can be used to search for treatment effectheterogeneity in high-dimensional datasets even where we lack a strong enoughtheoretical framework to select variables or make parametric assumptions aboutdata. This paper uses causal machine learning methods to estimate heterogeneoustreatment effects in the case of an experimental study carried out in Moroccowhich evaluated the effect of conditionalizing a cash transfer program onschool attendance compared to a labelled cash transfer. We show that there islittle heterogeneity in effects with the average treatment effect across threedifferent conditioning policies all being negative. We then explore if thereare any variables in the dataset of 1936 pre-treatment variables that areparticularly strong predictors of heterogeneity to try to understand thiseffect. While there are some variables we expected to be important here basedon our theoretical framework, most are atheoretical variables whose effects aredifficult to interpret. Household spending variables and child time-usevariables are particularly important, however no variables have particularlylarge effects. The second purpose of this paper is to demonstrate and reflectupon a causal machine learning approach to policy evaluation. In this vein wesuggest that findings that are difficult to interpret in this way are notsurprising given the atheoretical methodology. We reflect that causal machinelearning methods should not replace existing evaluation methodologies, butrather could be a useful tool for working with high-dimensional data andgenerating hypotheses.

Causal machine learning in public policy evaluation -- an application to the conditioning of cash transfers in Morocco