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

Author:

Patrick Rehill, Nicholas Biddle

Keyword:

Economics, General Economics, General Economics (econ.GN)

journal:

date:

2024-01-13 00:00:00

Abstract

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 school attendance compared to a labelled cash transfer. We show that there is little heterogeneity in effects with the average treatment effect across three different conditioning policies all being negative. We then explore if there are any variables in the dataset of 1936 pre-treatment variables that are particularly strong predictors of heterogeneity to try to understand this effect. While there are some variables we expected to be important here based on our theoretical framework, most are atheoretical variables whose effects are difficult to interpret. Household spending variables and child time-use variables are particularly important, however no variables have particularly large effects. The second purpose of this paper is to demonstrate and reflect upon a causal machine learning approach to policy evaluation. In this vein we suggest that findings that are difficult to interpret in this way are not surprising given the atheoretical methodology. We reflect that causal machine learning methods should not replace existing evaluation methodologies, but rather could be a useful tool for working with high-dimensional data and generating hypotheses.

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