TESIS PARA OPTAR AL GRADO DE MAGISTER EN ANÁLISIS ECONÓMICO / 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.
| Identifer | oai:union.ndltd.org:UCHILE/oai:repositorio.uchile.cl:2250/168527 |
| Date | 06 1900 |
| Creators | Balsa Fernández, Juan José |
| Contributors | Díaz Solis, David, Ronchetti, Diego |
| Publisher | Universidad de Chile |
| Source Sets | Universidad de Chile |
| Language | English |
| Detected Language | English |
| Type | Tesis |
| Rights | Attribution-NonCommercial-NoDerivs 3.0 Chile, http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ |
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