Martin Huber

Professor of Applied Econometrics and Policy Evaluation. Ph.D. in Economics and Finance (2010) and subsequently Assistant Professor at the University of St.Gallen (until 2014). Research stays at Harvard University (2011/2012) and the University of Sydney (2014 and 2019). Research interests: Data-based policy evaluation in labor, health, and education economics; further development of statistical/econometric methods for measuring causal effects; machine learning for forecasting and causal analysis.

Teaching and courses

• Big Data Methods
• Einführung in die Statistik II

Research interests

Data-based causal analysis; machine learning; policy/impact/treatment evaluation in labor, health, education, and business economics; algorithmic detection of collusion/cartels; semi- and nonparametric methods.

Academic positions

• Since 09/2014 University of Fribourg - Professor, Chair of Applied Econometrics and Policy Evaluation
• 02/2010-08/2014 University of St. Gallen - Assistant professor of quantitative methods in economics
• 04/2006 -01/2010 Swiss Institute for Empirical Economic Research, University of St. Gallen - Research assistant to Prof. Michael Lechner

Education

• 04/2006-01/2010 Ph.D. in Economics and Finance; University of St. Gallen, Switzerland Summa cum laude/with distinction. Specialization: Econometrics; thesis: "Microeconometric Estimators and Tests based on Nonparametric Methods, Quantile Regression, and Resampling";
• 10/1999-02/2004 M.A. programs in Economics and in International Business Studies; University of Innsbruck, Austria
• 09/2001 - 04/2002 Ecole Supérieure de Commerce, Grenoble, France : Erasmus study abroad program

Awards and grants

• 04/2018 Economicus 2017 prize by the foundation “Nadácia VÚB” (Slovakia) for economists below 40 awarded for the joint paper with Lukáš Lafférs and Giovanni Mellace “Sharp IV Bounds on Average Treatment Effects on the Treated and other Populations under Endogeneity and Noncompliance”.
• 11/2017 Best paper award on corruption issues of the organization “Obchestvo Znanie” for the joint paper with Elena Denisova-Schmidt and Elvira Leontyeva “Do Anti-Corruption Educational Campaigns Reach Students? Some Evidence from Two Cities in Russia and Ukraine”.
• 09/2017 Handelsblatt Ranking for Economists in and from German speaking countries: ranked 8th in terms of research output in the last 5 years; ranked 11th among economists below 40 in terms of total research output.
• 05/2014 Latsis Prize of the University of St. Gallen awarded for the research in the field of microeconometric methodology for causal analyses at the "dies academicus" of the University of St. Gallen, May 24 2014
• 05/2013 Austrian Young Economists Award awarded for the joint paper with Giovanni Mellace "Testing instrument validity for LATE identification based on inequality moment constraints" at the Annual Meeting of the Austrian Economic Society, May 10-11 2013 in Innsbruck
• 01/2013 Labour Prize in Theoretical or Applied Microeconometrics awarded for the joint paper with Giovanni Mellace "Relaxing monotonicity in the identification of local average treatment effects" at the Fifth Italian Congress of Econometrics and Empirical Economics, Jan 16-18 2013 in Genoa
• 09/2011-05/2012 Research fellowship of the Swiss National Science Foundation for visiting the Harvard Department of Economics (Cambridge, MA, USA). Scientific sponsor: Prof. Guido Imbens
• 05/2009 Austrian Young Economists Award awarded for the paper "Testing for covariate balance using nonparametric quantile regression and resampling methods" at the Annual Meeting of the Austrian Economic Society, May 22-23 2009 in Linz

Teaching

BA level: Statistics, Applied Econometrics. MA level: Econometric Methods and Applications, Policy and Impact Evaluation (causal analysis), Big Data Methods (machine learning and non-parametric methods). PhD level: Treatment evaluation based on instrumental variables, Introduction to matching estimators, Causal mediation analysis, Predictive and causal machine learning. Executive courses: Introduction to machine learning with R or KNIME, Introduction to causal analysis, Machine learningbased detection of collusion.