Martin Huber
martin.huber@unifr.ch
+41 26 300 8274
https://orcid.org/0000-0002-8590-9402
Statistik; datenbasierte Kausalanalyse; maschinelles Lernen (machine learning); Politikevaluation in der Arbeitsmarkt-, Gesundheits- und Bildungsökonomie; semi- und nichtparametrische Mikroökonometrie.
Ordentliche_r Professor_in
Departement für Volkswirtschaftslehre
Bd de Pérolles 90
1700 Fribourg
Biografie
Professor für Angewandte Ökonometrie und Politikevaluation. Ph.D. in Economics and Finance (2010) und anschliessend Assistenzprofessor an der Universität St.Gallen (bis 2014). Forschungsaufenthalte an der Harvard Universität (2011/2012) sowie and der Universität Sydney (2014 und 2019).
Affiliationen: Ausschuss für Ökonometrie des Vereins für Socialpolitik, Global Labor Organization, Soda Labs (Monash Business School), Zentrum für Europäische Wirtschaftsforschung (ZEW) Mannheim.
Forschungsinteressen: Daten-basierte Politikevaluation in der Arbeitsmarkt-, Gesundheits- und Bildungsökonomik; Weiterentwicklung von statistischen/ökonometrischen Methoden zur Messung von kausalen Effekten; Machinelles Lernen (machine learning) zur Vorhersage und Kausalanalyse.
Lebenslauf
Forschung und Publikationen
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Buchbeiträge
6 Publikationen
Mediationsanalyse , in Moderne Verfahren der Angewandten Statistik
Martin Huber (2024), ISBN: 9783662634967 | BuchkapitelCausal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R
Martin Huber (MIT Press, 2023) | BuchAn introduction to flexible methods for policy evaluation , in Handbook of Research Methods and Applications in Empirical Microeconomics
Huber, M. (2021) | BuchkapitelMediation Analysis , in Handbook of Labor, Human Resources and Population Economics
Martin Huber (2020) | BuchkapitelRecent Regional Economic Development in Ukraine: Does history help to explain the differences? , in Regionalism without regions
Martin Huber, Denisova-Schmidt E., Pohorila, N., Prytula, Y., Tyahlo, S (2019) | BuchkapitelCorruption among Ukrainian businesses: Do firm size, industry and region matter?
Denisova-Schmidt, E. and Huber, M. and Prytula, Y. (State Capture, Political Risks and International Business: Cases from Black Sea Region Countries, 2016) | Buch -
Publikationen
81 Publikationen
An Introduction to Causal Discovery
Swiss Journal of Economics ans Statistics (2024) | ArtikelThe Austrian Political Advertisement Scandal: Searching for Patterns of “Journalism for Sale”
(2024) | ArtikelThe Finite Sample Performance of Instrumental Variable-Based Estimators of the Local Average Treatment Effect When Controlling for Covariates
Computational Economics (2024) | ArtikelCausal Machine Learning in Marketing
International Journal of Business & Management Studies (2024) | ArtikelDouble Machine Learning for Sample Selection Models
Journal of Business & Economic Statistics (2024) | ArtikelA Wild bootstrap for propensity score matching estimators
Statistics & Probability Letters (2024) | ArtikelTesting Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data
Review of Economics and Statistics (2024) | ArtikelIt is never too LATE: a new look at local average treatment effects with or without defiers
The Econometrics Journal (2023) | ArtikelDoubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning
Hsu, Y.-C. and Huber, M. and Yen, Y.-M. , arXiv (2023) | SonstigesHow causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign
PLoS ONE (2023) | Artikel -
Working papers
10 Publikationen
Learning control variables and instruments for causal analysis in observational data
Apfel, Nicolas and Hatamyar, Julia and Huber, Martin and Kueck, Jannis, (2024) | Working PaperA joint test of unconfoundedness and common trends
Huber, Martin and Oeß, Eva-Maria, (2024) | Working PaperTesting identification in mediation and dynamic treatment models
Huber, Martin and Kloiber, Kevin and Laffers, Lukas, (2024) | Working PaperMachine Learning for Staggered Difference-in-Differences and Dynamic Treatment Effect Heterogeneity
Hatamyar, Julia and Kreif, Noemi and Rocha, Rudi and Huber, Martin, (2023) | Working PaperDoubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning
Hsu, Y.-C. and Huber, M. and Yen, Y.-M. , arXiv (2023) | SonstigesTreatment Effect Analysis for Pairs with Endogenous Treatment Takeup
Kormos, M. and Lieli, R.P. and Huber, M. , arXiv (2023) | SonstigesFrom Homemakers to Breadwinners? How Mandatory Kindergarten Affects Maternal Labour Market Outcomes
Gangl, S. and Huber, M. , SSRN (2022) | SonstigesTesting the identification of causal effects in observational data
Huber, M. and Kueck, J. , arXiv (2022) | SonstigesDetecting Grouped Local Average Treatment Effects and Selecting True Instruments
Apfel, N. and Farbmacher, H. and Groh, R. and Huber, M. and Langen, H. , arXiv (2022) | SonstigesHow war affects political attitudes: Evidence from eastern Ukraine
Martin Huber, Svitlana Tyahlo, (2016) | Working Paper -
Forschungsprojekte
Gender Occupational Segregation in the Swiss Apprenticeship Market: the Role of Employers in an Experimental Evaluation
Status: AbgeschlossenBeginn 01.03.2018 Ende 28.02.2019 Finanzierung SNF Projektblatt öffnen In this project, we seek to answer the question of whether chances of employment are identical for girls and boys applying for an apprenticeship position in Switzerland as measured by employers’ responses to applica-tions from equally qualified males and females. Differential performance in the labor market according to gender is well documented in the academic and popular press and a permanent fixture of everyday life. The causes – endogenous choice of women and families – or the result of (statistic, taste, or implicit) discrimination, or both – are far more difficult to pin down. This study aims to bring additional light to this important question by examining the earliest systematic labor market experience by individuals in developed economies: the process of applying for an apprenticeship position and the role employers might play in fostering occupational gender segregation. In order to identify whether employers take gender into consideration when evaluating employment applications, a correspondence test will be conducted. The study will select occupations that are commonly perceived as being male (or female), as well as other occupations that are viewed as more gender neutral, and will compare the success rate of both genders across those. The statistical comparison of success rates in invitations for interviews of males and females across these different occupation types will allow us to address the question of whether potential differential treatment is stereotypical in nature or otherwise systematic.