Description
This module will cover machine learning (ML) techniques Ìýand consider their applications to empirical economics. Coursework will include coding exercises, and so some prior programming experience is highly recommended. While students may use whichever language they like for assignments, solutions will be provided in R.
While ML is usually associated with prediction, ML methods can be adapted to problems of causal inference and the estimation of structural economic parameters. Techniques we will cover include penalized regression, tree-based methods, and deep learning. Some important applications include the estimation of heterogeneous treatment effects, estimation of average treatment effects under high-dimensional confounding, and instrumental variables estimation.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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