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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 onseveral outcomes including maths test scores which is the focus of this work.We explore treatment effects across a dataset of 1936 pre-treatment variables.For the most part, heterogeneity is modelled by two different factors,participation in education (at the baseline) and more general measures ofpoverty. Those who are more disadvantaged at the baseline benefit less from anytreatment. While conditioning generally has a negative effect this moredisadvantaged group is also hurt more by conditioning. The second purpose ofthis paper is to demonstrate and reflect upon a causal machine learningapproach to policy evaluation. We propose a novel causal tree method forinterpretable modelling of causal effects and reflect on the difficulty ofexplaining atheoretical results.

Heterogeneous treatment effect estimation with high-dimensional data in  public policy evaluation -- an application to the conditioning of cash  transfers in Morocco using causal machine learning