  Causal machine learning methods can be used to search for treatment effect
heterogeneity in high-dimensional datasets even where we lack a strong enough
theoretical framework to select variables or make parametric assumptions about
data. This paper uses causal machine learning methods to estimate heterogeneous
treatment effects in the case of an experimental study carried out in Morocco
which evaluated the effect of conditionalizing a cash transfer program on
several 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 of
poverty. Those who are more disadvantaged at the baseline benefit less from any
treatment. While conditioning generally has a negative effect this more
disadvantaged group is also hurt more by conditioning. The second purpose of
this paper is to demonstrate and reflect upon a causal machine learning
approach to policy evaluation. We propose a novel causal tree method for
interpretable modelling of causal effects and reflect on the difficulty of
explaining atheoretical results.
