Prof. Stefan Feuerrigel gave a talk on “Learning policies for decision-making with causal machine learning: The case of development financing”
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.
Two group members attended the 5th International Conference on System Reliability and Safety Engineering (SRSE 2023) in Beijing, China
Two group members attended the 5th International Conference on System Reliability and Safety Engineering (SRSE 2023) in Beijing, China from October 20-23, 2023, conjunction with the annual meeting of Institute for Quality and Reliability (IQR), Tsinghua University. The conference is sponsored by Tsinghua University, supported by National University of Singapore, organized by Institute for Quality and Reliability, Tsinghua University, co-organized by Department of Industrial Engineering, Tsinghua University, patrons with Beijing Institute of Technology, Harbin Institute of Technology, Nanjing University of Science and Technology, Qingdao University, Shanghai University, Shanghai Jiao Tong University, Northwestern Polytechnical University, Sun Yat-sen University, City University of Hong Kong, University of Alberta, etc.
Dr. Xiaoge Zhang delivered a talk on “Safety assessment and risk analysis of complex systems under uncertainty” at Nanjing University, China
This talk showcases two different strategies to assess and analyze the safety of air transportation system. In the first place, considering the rich information in the historical aviation accident events, we analyzed the accidents reported in the National Transporation Safety Board (NTSB) over the past two decades, and developed a large-scale Bayesian network to model the causal relationships among a variety of factors contributing to the occurrence of aviation accidents. The construction of Bayesian network greatly facilitates the root cause diagnosis and outcome analysis of aviation accident. Next, we analyze how to leverage deep learning to forecast flight trajectory. Using Bayesian neural network, we fully characterize the effect of exogenous variables on the flight trajectory. The predicted trajectory is then expanded to multiple flights, and used to assess safety based on horizontal and vertical separation distance between two flights, thus enabling real-time monitoring of in-flight safety.
