Essays on applied machine learning

In this Dissertation, we deal with a series of applications of machine learning in the fields of social and health sciences. We introduce a set of novelties in the traditional usage of machine learning algorithms for predictive and causal inference tasks. In Part 1, we explore the field of machine learning for causal inference and we introduce two innovative techniques that combine state-of-the-art machine learning algorithms with causal inference methodologies. In the first Chapter, we introduce a novel Bayesian tree-based methodology to draw causal inference on heterogeneous effects in quasi-experimental scenarios. In the second Chapter, we account for possible drawbacks of tree-based methodologies by proposing a composite algorithm with a high level of interpretability and precision. In Part 2, we introduce applications of machine learning predictive power to forecast students’ financial literacy scores and firm’s financial distress. In the third Chapter, we innovate the applied machine learning literature by proposing a novel sensitivity analysis for predictions. Finally, in the fourth Chapter, we show how economic intuition can boost the performance of machine learning algorithms. The Dissertation contributes to the literatures on causal and predictive machine learning mainly by: (i) extending the current framework to novel scenarios and applications (Chapter 1 - Chapter 3); (ii) introducing interpretability in the learning models (Chapter 1 - Chapter 2); (iii) developing a novel methodology to assess the robustness of predictions (Chapter 3); (iv) informing the choice of the technique used by specific economic knowledge on the field of investigation (Chapter 4). In the applied Sections of each Chapter, we answer policy relevant questions that pave the way to the usage of machine learning for targeted interventions.