Using causal tree algorithms with difference in difference methodology : a way to have causal inference in machine learning

Abstract

been for a long time one of the main focus of the economist around the world. At the same time, the development of different statistical methodologies have deeply helps them to complement the economic theory with the different types of data. One of the newest developments in this area is the Machine Learning algorithms for Causal inference, which gives them the possibility of using huge amounts of data, combined with computational tools for much more precise results. Nevertheless, these algorithms have not implemented one of the most used methodologies in the public evaluation, the Difference in Difference methodology. This document proposes an estimator that combines the Honest Causal Tree of Athey and Imbens (2016) with the Difference in Difference framework, giving us the opportunity to obtain heterogeneous treatment effect. Although the proposed estimator has higher levels of Bias, MSE, and Variance in comparison with the OLS, it is able to find significant results in cases where OLS do not, and instead of estimate an Average Treatment Effect, it is able to estimate a treatment effect for each individual.