Martin Huber
martin.huber@unifr.ch
+41 26 300 8274
https://orcid.org/0000-0002-8590-9402
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Professor,
Department of Economics
PER 21 bu. F421
Bd de Pérolles 90
1700 Fribourg
Statistics; data-based causal analysis; machine learning; policy evaluation in labor, health, and education economics; semi- and nonparametric microeconometrics.
Biography
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).
Affiliations: Committee for Econometrics of the Verein für Socialpolitik, Global Labor Organization, Soda Labs (Monash Business School), Centre for European Economic Research (ZEW) Mannheim.
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.
Curriculum vitae
Research and publications
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Book chapters
6 publications
Mediationsanalyse , in Moderne Verfahren der Angewandten Statistik
Martin Huber (2024), ISBN: 9783662634967 | Book chapterCausal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R
Martin Huber (MIT Press, 2023) | BookAn introduction to flexible methods for policy evaluation , in Handbook of Research Methods and Applications in Empirical Microeconomics
Huber, M. (2021) | Book chapterMediation Analysis , in Handbook of Labor, Human Resources and Population Economics
Martin Huber (2020) | Book chapterRecent 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) | Book chapterCorruption 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) | Book -
Publications
81 publications
An Introduction to Causal Discovery
Swiss Journal of Economics ans Statistics (2024) | Journal articleThe Austrian Political Advertisement Scandal: Searching for Patterns of “Journalism for Sale”
(2024) | Journal articleThe Finite Sample Performance of Instrumental Variable-Based Estimators of the Local Average Treatment Effect When Controlling for Covariates
Computational Economics (2024) | Journal articleCausal Machine Learning in Marketing
International Journal of Business & Management Studies (2024) | Journal articleDouble Machine Learning for Sample Selection Models
Journal of Business & Economic Statistics (2024) | Journal articleA Wild bootstrap for propensity score matching estimators
Statistics & Probability Letters (2024) | Journal articleTesting Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data
Review of Economics and Statistics (2024) | Journal articleIt is never too LATE: a new look at local average treatment effects with or without defiers
The Econometrics Journal (2023) | Journal articleDoubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning
Hsu, Y.-C. and Huber, M. and Yen, Y.-M. , arXiv (2023) | OtherHow causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign
PLoS ONE (2023) | Journal article -
Working papers
10 publications
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) | OtherTreatment Effect Analysis for Pairs with Endogenous Treatment Takeup
Kormos, M. and Lieli, R.P. and Huber, M. , arXiv (2023) | OtherFrom Homemakers to Breadwinners? How Mandatory Kindergarten Affects Maternal Labour Market Outcomes
Gangl, S. and Huber, M. , SSRN (2022) | OtherTesting the identification of causal effects in observational data
Huber, M. and Kueck, J. , arXiv (2022) | OtherDetecting 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) | OtherHow war affects political attitudes: Evidence from eastern Ukraine
Martin Huber, Svitlana Tyahlo, (2016) | Working paper -
Research projects
Gender Occupational Segregation in the Swiss Apprenticeship Market: the Role of Employers in an Experimental Evaluation
Status: CompletedStart 01.03.2018 End 28.02.2019 Funding SNSF Open project sheet 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.