Machine Learning for Prediction and Causal Analysis

The course provides an in-depth understanding of the foundations, scope and approaches of machine learning for prediction and causal analysis and it focuses on their application to problems in social sciences and economics. Starting from the basics of linear regression, which underlies many machine learning models, this course introduces students to highdimensional predictive and causal problems. In particular, this course provides students with the basic ideas and intuition behind modern machine learning methods as well as an understanding of how, why, and when they work in practice. Students in this course will not only gain a deep understanding of the foundational aspects of machine learning, but they will also acquire the practical skills necessary for their successful applications to real-world problems.

The course will also help students make judgments, and develop an in-depth, critical understanding of the scope and challenges of machine learning and data-driven analytics. Throughout the entire course, students will be invited to assess the strengths and weaknesses of all different methods presented in class. The difference between prediction of observable outcomes versus causal effect estimation of unobservable parameters will be discussed to understand that just correlation is not causation.

Finally, the course will help students improve their communications skills. This course will give the students the possibility to learn how to communicate science, namely, how to effectively present their ideas, findings, proposals, analysis and critical reasoning in the area of data-driven analytics. A special emphasis will be given to oral presentations and pitches in project group works, and to writing scientific papers.

Course leader

Marica Valente - University of Innsbruck

Target group

Like previous editions, the Summer School aims to reinforce the skills, in the theoretical and empirical fields, of postgraduate students (2nd and 3rd cycles) and also of researchers, national and foreign, in the area of Economics and Management.

Course aim

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Fee info

EUR 150: General public, early bird (until 15 May, 2024): €150 per course.
Students, early bird (until 15 May, 2024): €120 per course.
EUR 200: General public (after 15 May 2024): €200 per course.
Students (after 15 May, 2024): €160 per course.

Scholarships

no