Presented By: Dr. Melvyn Weeks (University of Cambridge)
This course will review the application of machine learning techniques to both prediction problems and so-called causal problems where a firm or policy maker needs to understand the impact of some form of intervention on a heterogeneous population. We contrast a modelling approach where the analyst makes certain assumption on model specification, including functional form, with an approach where the data mechanism is presumed unknown. In this context we consider the econometrician’s concern for internal validity, alongside the focus within machine learning of ensuring that a model is robust in the sense of generalising to unseen data (external validity).
The course will focus upon topics at the intersection of machine learning and econometrics, covering a mix of theory and applications. In making the distinction between models which are used to solve a prediction problem and models which are used to estimate some form of causal effect, we introduce participants to identification strategies in econometrics. In covering two broad areas where machine learning is used, namely prediction, classification and causal effects, for each case we link the exposition to parametric bench- marks. For Machine Learning models in prediction, classification and causal effects we provide examples using Stata, R and Python.
