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• Macroeconomic Forecasting
• Machine Learning
• Econometrics
• +6 more

Jad Beyhum is an Associate Professor in Economics at KU Leuven, affiliated with the Faculty of Economics and Business and its Economics Research Group. He specializes in econometrics and statistics, focusing on high-dimensional data analysis, instrumental variable methods, and survival analysis techniques. Current research themes include high-dimensional econometrics (e.g., LASSO-type procedures, factor models) and instrumental variables techniques (e.g., dynamic treatments, censored data). His methodological contributions target econometric challenges in nonlinear panel data, competing risks models, and duration outcomes. Key applications span corporate bankruptcy prediction, program evaluation, and macroeconomic forecasting. Recent publications highlight innovations in instrumental variable estimation under censoring, testing treatment effect homogeneity, and combining machine learning with traditional econometric frameworks. He actively supervises projects on topics like "Beyond Instrumental Variables" and "New methods to control unobserved heterogeneity" . Selected project leadership roles: "Predicting bankruptcy with machine learning methods" (Promotor, 2023-2027) "Partial identification methods for survival analysis" (Co-promotor, 2023-2027)

• International Trade
• Macroeconomics
• Political Economy
• +5 more

Rodrigo Adao is an Associate Professor of Economics at the University of Chicago Booth School of Business, where he teaches courses in international commercial policy, international macroeconomics and trade, and workshop in macro and international economics. He also serves as a Faculty Research Fellow at the National Bureau of Economic Research and as an associate editor for the Journal of International Economics. His educational background includes a PhD in economics from MIT, as well as a BA and MA in economics from Pontifical Catholic University of Rio de Janeiro (PUC-Rio). Prior to joining Chicago Booth, he held positions as a research scholar at the Becker-Friedman Institute and an IES Research Fellow at Princeton University. Adao's primary research field is international trade, with a focus on the effect of globalization on welfare and inequality. His work spans theoretical and empirical approaches to understanding trade policy, trade elasticities, and the distributional consequences of international trade. He has developed novel methodologies for analyzing trade protection, firm heterogeneity in trade models, and the spatial implications of trade shocks. His research consistently addresses how trade policies affect different segments of society, with particular attention to inequality patterns and welfare distribution across sectors and regions. His publication record reveals a strong methodological focus, with contributions to econometric techniques for testing trade models, measuring the impact of trade shocks, and analyzing spatial economic relationships. A notable trend in his recent work involves developing more sophisticated approaches to understanding the political economy of trade protection and evaluating the accuracy of quantitative trade models against real-world data. As a Faculty Research Fellow at the National Bureau of Economic Research, Adao contributes to one of the most prestigious economic research organizations in the United States. His role as associate editor for the Journal of International Economics places him at the forefront of scholarly discourse in his field. Adao has contributed significantly to academic discourse through his research publications in top journals including the American Economic Review and The Quarterly Journal of Economics. His work has been supported by research institutions including the National Bureau of Economic Research, and he has been featured as a speaker on the Review of Economic Studies Tour. While specific laboratory or dedicated research team information isn't provided in the available text, Adao's extensive publication record across multiple specialized areas of international trade suggests active collaboration with other economists and likely supervision of research assistants working on his various projects.

John PepperView profile

Professor

• Public Economics
• Econometrics
• Social Program Evaluation
• +7 more

John Pepper is the Merrill S. Bankard Professor of Economics and currently serves as Interim Chair in the Department of Economics at the University of Virginia, College of Arts & Sciences. His research focuses on public economics, econometrics, and the evaluation of social programs using advanced statistical methods to address data limitations. Research Interests: His work centers on partial identification, misreporting in survey data, and causal inference in policy evaluation. Key areas include vocational rehabilitation, food security (SNAP, WIC), mental and physical disability, gun laws, and intergenerational welfare transmission. He develops and applies econometric techniques such as bounded-variation assumptions and monotone instrumental variables to draw robust conclusions under uncertainty. Publication Trends: Over the past two decades, his research has consistently addressed public policy questions using rigorous econometric frameworks. Recent publications (2020–2024) emphasize mental health, food security, gun policy, and disability, often employing partial identification to handle data ambiguity. His collaborations with leading economists like Charles Manski and Brent Kreider reflect his influence in methodological and applied economics. Best Economics Paper Award, Food Safety and Nutrition section of the Agricultural & Applied Economics Association, Honorable Mention, 2024 Advising and Grants: While specific students are not listed, he mentors through collaborative research and large-scale evaluations. He has contributed to major national reports for the National Research Council on topics including the death penalty, firearms violence, and illegal drugs. His work on vocational rehabilitation includes a forthcoming 2025 Springer book on return-on-investment models, indicating sustained funding and institutional support. Labs and Research Teams: He is part of collaborative research teams focused on public policy evaluation, often working with multidisciplinary groups from institutions such as the National Research Council. His projects involve complex data analysis from sources like the Panel Study of Income Dynamics (PSID) and RSA-911, and he contributes to working paper series and policy reports that inform government and academic discourse.

Otavio Camargo BartalottiView profile

Associate Professor

• Applied Econometrics
• Labour Economics
• Policy Evaluation
• +6 more

Otavio Camargo Bartalotti is an Associate Professor in the Department of Econometrics & Business Statistics at Monash University's Faculty of Business and Economics. He is actively engaged in research and teaching in econometrics and is accepting PhD students. Education: Ph.D. in Economics, Michigan State University (2012) Master of Arts in Economics, Michigan State University (2008) Master of Science in Economics, Fundação Getulio Vargas (2007) Bachelor of Science in Economics, University of São Paulo (2004) His research centers on applied econometrics, with a focus on causal inference, treatment effects, and policy evaluation in labor economics. He develops and applies methods such as regression discontinuity designs and instrumental variable models to address identification challenges in observational data. His work often addresses measurement error and sample selection issues, contributing to more reliable empirical findings. His recent publications in top journals like the Journal of Econometrics and American Economic Journal: Economic Policy reflect a consistent focus on methodological rigor and policy relevance. These works span topics including marginal treatment effects, bivariate probit models, and the impact of unemployment insurance, demonstrating strong technical expertise and real-world applicability. He is a Research Fellow at IZA – Institute of Labor Economics, indicating ongoing international collaboration and influence in labor economics research. He has advised on policy through research that has been referenced in policy sources. His work bridges econometric theory and practical policy analysis, particularly in labor market interventions. He leads or contributes to collaborative research projects, often co-authoring with prominent scholars in econometrics and microeconomics.

Ingrid VAN KEILEGOMView profile

Professor

• Survival Analysis
• Duration Analysis
• Measurement Error
• +5 more

Ingrid VAN KEILEGOM is a Full Professor at the Catholic University of Louvain (UCL), affiliated with the Institute of Statistics, Biostatistics and Actuarial Sciences. Her research spans survival analysis, econometrics, nonparametric methods, and copula theory, with significant contributions to statistical methodology for censored data and regression models. Research Expertise Professor Van Keilegom's work focuses on developing innovative statistical methods for complex data structures. Key areas include: Survival/duration analysis with cure models and dependent censoring Econometric techniques for instrumental variables and frontier models Semiparametric regression for measurement error problems Copula-based dependence modeling in multivariate settings Publication Trends Her recent publications (2015-2016) demonstrate a strong focus on semiparametric methods for censored data, frontier estimation in econometrics, and goodness-of-fit testing. Common themes include developing robust inference procedures for survival models, addressing endogeneity in econometric applications, and creating validation techniques for complex regression structures. Awards and Honors Fellow, Institute of Mathematical Statistics (2008) Fellow, American Statistical Association (2013) ERC Advanced Grant (2016-2021): Semiparametric inference for complex structural models ERC Starting Grant (2008-2014): M-/Z-estimation in semiparametric statistics Academic Leadership She supervises 5 doctoral students and has graduated 7 PhDs since 2005. Her editorial leadership includes serving as Joint Editor for Journal of the Royal Statistical Society - Series B (2012-2015) and associate roles in 6 other statistical journals. Research is supported by major grants including two European Research Council awards.

Felix ElwertView profile

Professor

• Social Inequality
• Causal Inference
• Social Demography
• +3 more

Felix Elwert is the Vilas Distinguished Achievement Professor of Sociology at the University of Wisconsin-Madison, with a joint appointment in Biostatistics. He specializes in causal inference methodologies and investigates social inequality, demography, and health disparities through large-scale experiments, population registers, and survey analyses. His work bridges sociology, statistics, and public health, with notable contributions to understanding peer effects, neighborhood effects, and methodological innovations in causal analysis. Elwert holds a Ph.D. in Sociology (2007) and M.A. in Statistics (2006) from Harvard University. Before his current position, he served as Karl W. Deutsch Professor and Acting Director of Social Inequality and Social Policy at the WZB Berlin Social Center (2014–2016). He currently leads the Sociological Methods & Research journal as Editor-in-Chief. His research focuses on: Contextual drivers of inequality in income, education, and health Causal mediation analysis Experimental designs for estimating peer effects Bias detection in statistical models Elwert has received prestigious awards including the ASA's Causality in Statistics Education Award (first recipient) and the ASA's Leo Goodman Award. His collaborations span institutions globally, emphasizing interdisciplinary approaches to causal questions. He teaches advanced causal inference courses at UW-Madison and international universities, covering topics like graphical models, mediation analysis, and experimental design. His campus affiliations include the Center for Demography and Ecology, Center for Demography of Health and Aging, and Institute for Diversity Science.

Chu-An LiuView profile

Research Fellow

• Econometrics
• Model Averaging
• Forecast Combination
• +3 more

Chu-An Liu is a Research Fellow at the Institute of Economics, Academia Sinica (Taipei, Taiwan). He received a PhD in Economics from the University of Wisconsin-Madison in 2012. His teaching experience includes graduate-level and PhD econometrics courses at National Chengchi University (Spring 2022-present) and National University of Singapore (2013-2015). Research Interests: His work focuses on econometrics , model averaging , and forecast combination , with significant contributions to nonparametric methods, causal inference, and statistical modeling for high-dimensional economic data. Key areas include bounds estimation for continuous treatments, spectral analysis in time series, and model uncertainty frameworks. Publications: He has published extensively in top journals like Journal of Econometrics , Econometric Theory , and Journal of Business & Economic Statistics . Recent trends emphasize kernel regressions, autoregressive models, and integration of machine learning with traditional econometric techniques. Academic Collaborations: He has collaborated with scholars such as Xinyu Zhang, Ying-Ying Lee, and Biing-Shen Kuo on topics spanning model selection, causal inference, and nonstationary data analysis.

Mao XiaojieView profile

Associate Professor

• Causal inference
• Data-driven optimization decision-making
• Machine Learning
• +3 more

Mao Xiaojie is an Associate Professor at the Department of Management Science and Engineering, Tsinghua University's School of Economics and Management . Holding a PhD in Statistics and Data Science from Cornell University (2021) and a bachelor's in Mathematical Economics and Finance from Wuhan University (2016), Mao specializes in causal inference and data-driven optimization decision-making . PhD: Cornell University (2016-2021) Bachelor: Wuhan University (2012-2016) Mao's research bridges machine learning , statistics , and operations research to address challenges in contextual optimization, algorithmic fairness, and robust causal inference. Recent work focuses on data combination , surrogate variables , and minimax methods for handling unobserved confounding and limited outcome data. Key trends in Mao's publications include: Advancing bandit algorithms for efficient contextual decision-making Developing debiased machine learning frameworks for quantile treatment effects Designing robust optimization models under noisy and incomplete covariates Scientific recognition includes: Applied Probability Society Best Student Paper Competition Finalist (2020) Multiple teaching excellence awards at Tsinghua University (2022-2024) Research grants from the National Natural Science Foundation of China Mao currently teaches Empirical Methods in Management Science (PhD), Data Analysis: Inference and Decision Making (Master), and Probability Theory and Mathematical Statistics (Undergraduate). Research collaborations span institutions like Cornell and MIT, with publications in top venues including NeurIPS , ICML , and Operations Research .

• Theoretical econometrics
• Semi-nonparametric estimation and testing
• Machine learning methods for causal inference
• +3 more

Elia Lapenta is an Assistant Professor in Economics at ENSAE-CREST (National School of Statistics and Economics). His work focuses on theoretical econometrics and causal inference, with specific expertise in hypothesis testing, nonparametric instrumental variables estimation, and empirical games with incomplete information. Education PhD in Economics from Toulouse School of Economics (2020) Research Interests His research spans econometric theory and applied methods, including: Semi-nonparametric estimation and testing Machine learning for causal inference High-dimensional econometrics Empirical games with incomplete information

Qiankun ZhouView profile

Professor

• Theoretical Econometrics
• Applied Econometrics
• Panel Data Econometrics
• +5 more

Qiankun Zhou is a Professor of Economics at Louisiana State University, holding the prestigious Freeport-McMoran Endowed Professorship in Economics #2. Based in the Business Education Complex South (2321) in Baton Rouge, LA, he can be contacted at qzhou@lsu.edu or 225-578-5211. Professor Zhou earned his PhD in Economics from the University of Southern California in 2015. His academic journey reflects rapid advancement to full professorship, underscored by his endowed chair position. His research program bridges theoretical and applied econometrics with exceptional focus on panel data methodologies. Key contributions include breakthroughs in treatment effects estimation with interactive fixed effects, high-dimensional inference through orthogonal projection, and specification testing for time-varying coefficient models. His work consistently addresses the tension between methodological rigor and practical applicability in economic analysis. Recent publications (2023-2025) reveal intensified exploration of causal inference frameworks and high-dimensional data challenges, with multiple papers in Journal of Econometrics and Econometric Theory . This output demonstrates sustained theoretical innovation while maintaining strong empirical relevance. His primary distinction is the Freeport-McMoran Endowed Professorship in Economics #2, recognizing exceptional research contributions at LSU. Professor Zhou maintains active research collaborations across institutions including USC, Florida State University, and international partners. His advising likely emphasizes mathematical rigor coupled with real-world application, preparing students for research careers through co-authorship opportunities on methodological advances. The absence of a named lab reflects economics' typical research structure where theoretical work occurs through individual and small-group collaboration.

Matthew Joseph KusnerView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Pattern Analysis
• +5 more

Matt J. Kusner is an Associate Professor in the Department of Computer Engineering and Software Engineering at Polytechnique Montréal. He holds additional affiliations as a Senior Academic Member at Mila - Quebec Artificial Intelligence Institute and as a Member at the Institute for Data Valorization (IVADO). Previously, he served as an Associate Professor at University College London and the University of Oxford. Professor Kusner's research spans machine learning, with particular focus on causal inference, fairness in algorithms, representation learning, and domain adaptation. His work bridges theoretical foundations with practical applications in natural language processing, algorithmic fairness, and scientific computing domains including plasma physics and renewable energy systems. His research interests include Pattern Analysis and Artificial Intelligence, Algorithms, and Learning and Inference Theories. His publication record shows consistent output across top machine learning venues including NeurIPS, ICML, and ICLR, with recent work trending toward causal machine learning methods and applications in scientific domains. Professor Kusner has 42 publications spanning from 2014 to 2025, demonstrating sustained research productivity. Turner Dissertation Award for best doctoral dissertation in Computer Science & Engineering Professor Kusner received his PhD in Computer Science from Washington University in St. Louis in 2016 under Kilian Weinberger. His work has been featured in major media outlets including The Guardian, Forbes, and the Harvard Business Review, and he has presented at prestigious institutions including the Federal Reserve Banks, Cambridge Centre for Mathematical Sciences, and the Royal Society.

Gerard J van den BergView profile

Professor

• Econometrics
• Labor Economics
• Health Economics
• +7 more

Gerard J van den Berg is an Honorary Professor at the University of Bristol's School of Economics and is affiliated with the Bristol Population Health Science Institute. A Fellow of the Econometric Society since 2013, he is recognized as a world leader in econometrics with significant contributions to labor economics and health economics. His research focuses on duration analysis, treatment evaluation, search theory, and long-run effects of early-life conditions . His work spans both theoretical econometric methodology and applied economic questions, with particular expertise in analyzing large-scale register data, especially from Sweden. Professor van den Berg has published extensively in top journals including Econometrica , Review of Economic Studies , American Economic Review , and Journal of the Royal Statistical Society Series B . His recent work (through 2025) shows continued active research in prenatal exposure effects, education impacts on health, and labor market policy evaluation. Fellow of the Econometric Society (2013) Former Joint Managing Editor of The Economic Journal Former Associate Editor of Journal of Econometrics Former Associate Editor of Journal of Population Economics His research has garnered significant attention, with policy citations, news mentions, and substantial readership across academic platforms. His work bridges econometric methodology with substantive questions in labor and health economics, demonstrating both theoretical rigor and practical relevance to economic policy.

• Causal Machine Learning
• Mechanistic Machine Learning
• Dynamical Systems
• +6 more

Niki Kilbertus is a Professor in the Department of Informatics at the Technical University of Munich and a group leader at Helmholtz AI (Helmholtz Munich). They are also affiliated with MCML, the Konrad Zuse School relAI, and the Munich Unit of ELLIS. Since 2024, they have been a member of the Junge Akademie and received the Leopoldina Prize for Young Scientists. In 2025, they were awarded an ERC Starting Grant and achieved tenure at TUM. Professor Kilbertus's research focuses on causal machine learning, mechanistic ML, dynamical systems, and AI for science. Their work spans theoretical foundations of causal inference and practical applications across scientific domains. They have made significant contributions to causal effect estimation, causal discovery in stochastic processes, learning differential equations, and fair machine learning. Their research often bridges computer science with physics, biology, and climate science, demonstrating the interdisciplinary nature of their work. Professor Kilbertus has published extensively in top machine learning venues including NeurIPS, ICML, and ICLR, with numerous publications in 2024-2025. Their recent work shows a strong trend toward causal discovery in continuous-time systems, intervention modeling, and physics-informed machine learning applications. Scientific Awards: Leopoldina Prize for Young Scientists (2024) ERC Starting Grant (2025) Professor Kilbertus actively supervises multiple PhD students and collaborates with researchers across institutions including Max Planck Institutes and Helmholtz centers. They serve as an Action Editor for TMLR and regularly review for major ML conferences. The research group is well-funded through the ERC grant and institutional support from TUM and Helmholtz AI, enabling active recruitment of new PhD students and postdocs. Based at Technical University of Munich and Helmholtz AI, Professor Kilbertus's team works at the intersection of theoretical machine learning and scientific applications, with particular strengths in causal reasoning for complex dynamical systems.

Michael Charles SachsView profile

Associate Professor

• Biostatistics
• Causal Inference
• Survival Analysis
• +3 more

Michael Charles Sachs serves as Associate Professor in the Department of Public Health, Section of Biostatistics at the University of Copenhagen's Faculty of Health and Medical Sciences. He holds a Doctorate in Biostatistics from the University of Washington (awarded August 15, 2011) and maintains an active research program with publications spanning 2022-2025. His primary research focuses on biomarker signatures for treatment selection , computational methods for deriving symbolic bounds on causal effects , and regression modeling of cumulative estimands with censored event history data . His general areas of interest include reproducible research, computational statistics, R programming, Bayesian nonparametric models, and survival analysis. Professor Sachs teaches the advanced course "Programming and Statistical Modeling in R" for PhD students and health researchers, covering programming principles, loops, functions, and efficient data manipulation. His recent publications demonstrate expertise in causal inference methodology, survival analysis techniques, and statistical software implementation, with multiple articles accepted for publication in 2025. His work spans theoretical methodology development and practical applications in medical research, particularly in gastroenterology and celiac disease prediction. His scholarly output shows consistent productivity with multiple first-author publications in high-impact biostatistics journals including Journal of Statistical Software, Journal of Computational and Graphical Statistics, and Biostatistics. He actively organizes academic activities, including a May 2025 workshop on statistical modeling in R.