Learning policies for decision-making with causal machine learning: The case of development financing
Distinguished Research Seminar Series
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Date
25 Oct 2023
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Organiser
Department of Industrial and Systems Engineering, PolyU
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Time
17:00 - 18:30
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Venue
Online via Zoom
Speaker
Prof. Stefan Feuerriegel
Remarks
Meeting link will be sent to successful registrants
Summary
The Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by “leaving no one behind”, and, to achieve the SDGs by 2030, poor countries require immense volumes of development aid. In this work, we develop a causal machine learning framework for estimating heterogeneous treatment effects of aid disbursements that inform optimal aid allocation. We demonstrate the effectiveness of our method using data with official development aid earmarked to end HIV/AIDS in 105 countries, amounting to more than USD 5.2 billion. For this, we first show that our method successfully computes heterogeneous treatment-response curves using semi-synthetic data. Then, using real-world HIV data, we find that an optimal aid allocation suggested by our method could reduce the total number of new HIV infections compared to current allocation practice. Our findings indicate the effectiveness of causal machine learning to inform cost-efficient allocations of development aid that maximize progress towards the SDGs.
Keynote Speaker
Prof. Stefan Feuerriegel
Professor, Head of Institute
Institute of Artificial Intelligence (AI) in Management
LMU Munich, Munich, Germany
Stefan Feuerriegel heads the new Institute of Artificial Intelligence (AI) in Management. He holds a dual affiliation as a full professor at LMU Munich School of Management and the Faculty of Mathematics, Informatics, and Statistics at LMU Munich. Previously, Stefan was an assistant professor at ETH Zurich. He graduated in 2015 with a Ph.D. at the Chair for Information Systems Research (Prof. Dr. Dirk Neumann), University of Freiburg. During his research stays, he partnered with researchers from the University of New South Wales (UNSW), Sydney, the National Institute of Informatics (NII), Tokyo, McCombs School of Business at the University of Texas at Austin, and Carnegie Mellon University (CMU), Pittsburgh. Stefan has co-authored 70+ journal articles and 80+ peer-reviewed conference papers. These works have appeared in top outlets from general science (e.g., PNAS, Nature Human Behaviour, Nature Machine Intelligence), management (e.g., Management Science, Marketing Science) and machine learning (e.g., ICML, ICLR, WWW, KDD, ACL, EMNLP, AAAI). According to Google Scholar, his works count 3800+ citations. Stefan currently serves as methodological expert for the Academy of Management Journal (AMJ). His group is supported by various companies (e.g., Google, Microsoft, Oracle, Nvidia, Amazon) and multiple grants, for which the funding volume totals to more than EUR 5.5 million. In particular, he received an SNSF Eccellenza Grant, which is the equivalent in Switzerland to the ERC Starting Grant.
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