2024-05-182024-05-1820678https://scholars.lib.ntu.edu.tw/handle/123456789/718982Jau-er Chen is currently the Cathay Financial Holdings Elite Professor at the School of Political Science and Economics, National Taiwan University. After completing his Ph.D. in economics at New York University in 2011, he held an Assistant Professor position at the Department of Economics, National Taiwan University (tenured in 2018), followed by Associate Professor position at the Institute for International Strategy at Tokyo International University, and subsequently an Associate Professor position before becoming Professor of Economics at Senshu University (Tokyo, Japan). He has also held visiting scholar positions at Columbia University in the City of New York, and MIT Economics Department at the Massachusetts Institute of Technology. In addition, he has served as adjunct faculty teaching courses for the Japan-IMF Scholarship Program, a policy paper supervisor at the National Graduate Institute for Policy Studies (Tokyo, Japan), and a research affiliate at National Taiwan University (2018–2025). His research focuses on econometrics, causal machine learning in economics, and applied finance, with work appearing in the Journal of Econometrics, Empirical Economics, WIREs Computational Statistics, and other publications. Jau-er has extensive experience teaching international students at a wide range of institutions, including liberal arts colleges, international universities, and research-intensive universities in Taiwan, Japan, and the United States. He teaches in Mandarin, English, and Japanese, covering topics such as Causal Machine Learning, Econometrics, Financial Economics, and Macroeconomics. To date, he has supervised 23 undergraduate and master’s theses, among which 10 of his students have gone on to pursue doctoral studies at leading universities in the United States, Europe, and Japan, with several already having obtained their Ph.D. degrees.Causal machine learning in economicsQuantile models with endogeneityApplied econometrics