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Machine Learning for Economists & Social Scientists

Machine learning is programming computers to build predictive models for making inference from samples for accurate out-of-sample predictions and decisions. As an applied economist, or researcher in a related field, if you feel that you need to learn these contemporary machine learning methods, implement them into your research, or looking for articles applied these methods to incorporate into the courses that you are teaching, this course is for you. In this course, participants will learn the fundamentals of machine learning and their implementation, recalling the fundamental statistical concepts at the heart of modern learning techniques. Participants will learn the differences between causal and predictive analyses and their relative merits, as well as their use in applied social sciences.

The course will combine both real data and theoretical background to enable researchers to gain practical experience in analysing a wide variety of data and econometric problems. It will also discuss how contemporary approaches in applied econometrics can be used to answer important questions in Economics. Participants will be provided a wealth of research papers and sources which apply the techniques being taught. Applications covered during the course will include the fields of labour, development, industrial organisation, health, macro economics and finance.

By the end of the course, participants will be able to understand various concepts intensively used in the machine learning literature such as cross-validation, bootstrapping, boosting, bagging, optimization routines, and identify common techniques, such as CART, Random Forest, MARS, GAM, Lasso, most suitable for their research questions and data and their natural extensions to causal inference with observational data.

The participants will be expected to be familiar with concepts and practices of traditional multivariate regression analysis, and related estimation and causal methods.

The lecture sessions will be July 20-22, there will be a webinar series on July 23-24th

All examples will be given in R or Stata, so you should also have a working knowledge of these software (which can be replaced by an advanced knowledge of another programming language, such as Python, to adapt the codes and exercises on your own). Participants are invited to bring their own laptop with R-Studio and Stata installed on it.

Register here (registration is required)

20 July, 2020 to 24 July, 2020
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