Martin Huber
martin.huber@unifr.ch
+41 26 300 8274
https://orcid.org/0000-0002-8590-9402
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Professeur·e ordinaire,
Département d'économie politique
PER 21 bu. F421
Bd de Pérolles 90
1700 Fribourg
Statistique ; analyse causale basée sur des données ; apprentissage machine (machine learning) ; évaluation des politiques en matière d'économie du travail, de la santé et de l'éducation ; microéconométrie semi- et non-paramétrique.
Biographie
Professeur d'économétrie appliquée et d'évaluation des politiques. Ph.D. en Economics and Finance (2010) et ensuite professeur assistant à l'Université de Saint-Gall (jusqu'en 2014). Séjours de recherche à l'Université de Harvard (2011/2012) et à l'Université de Sydney (2014 et 2019).
Affiliations : Comité d'économétrie du Verein für Socialpolitik, Global Labor Organization, Soda Labs (Monash Business School), Centre de recherche économique européenne (ZEW) Mannheim.
Intérêts de recherche : Évaluation empirique des politiques dans les domaines de l'économie du marché du travail, de la santé et de l'éducation ; développement de méthodes statistiques/économétriques pour mesurer les effets causaux ; apprentissage automatique (machine learning) pour la prévision et l'analyse causale.
CV
Recherche et publications
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Articles de livres
6 publications
Mediationsanalyse , dans Moderne Verfahren der Angewandten Statistik
Martin Huber (2024), ISBN: 9783662634967 | Chapitre de livreCausal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R
Martin Huber (MIT Press, 2023) | LivreAn introduction to flexible methods for policy evaluation , dans Handbook of Research Methods and Applications in Empirical Microeconomics
Huber, M. (2021) | Chapitre de livreMediation Analysis , dans Handbook of Labor, Human Resources and Population Economics
Martin Huber (2020) | Chapitre de livreRecent Regional Economic Development in Ukraine: Does history help to explain the differences? , dans Regionalism without regions
Martin Huber, Denisova-Schmidt E., Pohorila, N., Prytula, Y., Tyahlo, S (2019) | Chapitre de livreCorruption 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) | Livre -
Publications
81 publications
An Introduction to Causal Discovery
Swiss Journal of Economics ans Statistics (2024) | ArticleThe Austrian Political Advertisement Scandal: Searching for Patterns of “Journalism for Sale”
(2024) | ArticleThe Finite Sample Performance of Instrumental Variable-Based Estimators of the Local Average Treatment Effect When Controlling for Covariates
Computational Economics (2024) | ArticleCausal Machine Learning in Marketing
International Journal of Business & Management Studies (2024) | ArticleDouble Machine Learning for Sample Selection Models
Journal of Business & Economic Statistics (2024) | ArticleA Wild bootstrap for propensity score matching estimators
Statistics & Probability Letters (2024) | ArticleTesting Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data
Review of Economics and Statistics (2024) | ArticleIt is never too LATE: a new look at local average treatment effects with or without defiers
The Econometrics Journal (2023) | 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) | AutreHow causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign
PLoS ONE (2023) | 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) | AutreTreatment Effect Analysis for Pairs with Endogenous Treatment Takeup
Kormos, M. and Lieli, R.P. and Huber, M. , arXiv (2023) | AutreFrom Homemakers to Breadwinners? How Mandatory Kindergarten Affects Maternal Labour Market Outcomes
Gangl, S. and Huber, M. , SSRN (2022) | AutreTesting the identification of causal effects in observational data
Huber, M. and Kueck, J. , arXiv (2022) | AutreDetecting 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) | AutreHow war affects political attitudes: Evidence from eastern Ukraine
Martin Huber, Svitlana Tyahlo, (2016) | Working paper -
Projets de recherche
Gender Occupational Segregation in the Swiss Apprenticeship Market: the Role of Employers in an Experimental Evaluation
Statut: TerminéDébut 01.03.2018 Fin 28.02.2019 Financement FNS Voir la fiche du projet 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.