Estimating Partial Effects Using Machine Learning

Abstract

In this paper, we explore the use of machine learning techniques for estimating partial derivatives, which is a critical step towards understanding causal relationships in econometric analysis. By leveraging modern machine learning methods, such as tree-based models and deep neural networks, we assess their effectiveness in recovering regression functions and estimating partial derivatives. We introduce a novel tree-based model, Boosting Smooth Transition Regression Trees (BooST), and compare its performance with other models, including Boosting of Symmetric Smooth Additive Regression Trees (SMARTboost) and deep neural networks (DNNs). Simulations, based on the well-known Friedman data generating process (DGP), demonstrate the superiority of BooST in estimating partial effects across various signal-to-noise environments and in the presence of redundant variables. The empirical applications, including the study of Engel curves, further highlight the ability of BooST to outperform other machine learning models in accurately estimating partial derivatives. Our findings suggest that BooST provides a powerful tool for nonparametric regression and causal inference, especially in econometric contexts where accurate estimation of marginal effects is crucial.