Martin Huber
@CausalHuber
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Professor of Applied Econometrics and Policy Evaluation at @ses_unifr @unifr - causal analysis, statistics, econometrics, machine learning...and telemarking
Fribourg/Freiburg
Joined September 2019
🚀Registration is open for the #Fribourg #WinterSchool in #DataAnalytics & #MachineLearning, Feb 2–13 2026, hybrid at @ses_unifr or online. Topics: data analytics, predictive/causal machine learning, deep learning using Python, R, Julia, Knime. 👉 Sign up:
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😀 Attending the World Congress of the Econometric Society in the stunning city of Seoul, and thrilled to present joint work with N Apfel, J Hatamyar, & J Kueck on machine learning–based testing of conditions sufficient for identifying treatment effects:
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Excited to share our working paper “Machine Learning for Detecting Collusion and Capacity Withholding in Wholesale Electricity Markets”, joint with Jeremy Proz. We propose a machine learning–based approach for detecting cartels in electricity markets:
arxiv.org
Collusion and capacity withholding in electricity wholesale markets are important mechanisms of market manipulation. This study applies a refined machine learning-based cartel detection algorithm...
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Very happy to be teaching a @gesistraining workshop on causal inference with instrumental variables and regression discontinuity designs on October 9–10, 2025. Registration is still open:
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🎉 Seems like the release of "Impact Evaluation in Firms and Organizations" is off to a great start! Huge thanks to everyone who's been reading, sharing, and supporting my book!.
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This moment brings back memories from 2 years ago, when my 1st book, Causal Analysis, was released. It’s a comprehensive MA/Ph.D.-level textbook on impact evaluation and causal machine learning, with use cases in R (and Python versions available online):
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📘 My book Impact Evaluation in Firms and Organizations is officially out today with @mitpress! An accessible, non-technical introduction to impact evaluation (& causal machine learning) designed for practitioners & students, with use cases in R & Python:
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Delighted that our working paper “Catching Bid-rigging Cartels with Graph Attention Neural Networks”, joint work with D. Imhof and E. Viklund, is out! We propose a novel #DeepLearning algorithm based on GATs to detect collusive behavior in markets/tenders:
arxiv.org
We propose a novel application of graph attention networks (GATs), a type of graph neural network enhanced with attention mechanisms, to develop a deep learning algorithm for detecting collusive...
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And here it is! Just received my author copies of my book Impact Evaluation in Firms and Organizations, out with @mitpress on Aug 5. It offers a compact overview of methods for evaluating interventions, like marketing campaigns, with R and Python examples:
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Delighted to attend the second edition of the Causal Machine Learning Workshop in the UK, this time hosted at Queen Mary University of London. Many thanks to my co-author Michel Haddad and the co-organizers for putting together such a fantastic event! #MachineLearning
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Visiting the @ZEW (Leibniz Centre for European Economic Research) in #Mannheim this week and delighted to teach a PhD course on machine learning methods as part of their summer school! #DataScience #MachineLearning
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Attending the International Association for Applied Econometrics (IAAE) conference this week in beautiful Torino. Excited to present joint work with N. Apfel, J. Hatamyar, and Jannis Kueck on testing identifying assumptions in causal inference:
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Great to be visiting the Chair for Entrepreneurship at @UZH_ch for a seminar on data analytics and machine learning. Many thanks to Prof. Ulrich Kaiser and his team for the warm welcome and generous hospitality! #DataScience
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Visiting the Centre d'études et de recherches sur le développement international (@cerdi) in Clermont-Ferrand this week, a leading center for development economics. Many thanks for the warm welcome and the stimulating discussions on impact evaluation and causal machine learning!
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🚀 Less than 3 months to go! My book "Impact Evaluation in Firms and Organizations" (@mitpress) is now available for pre-order: It features intuitive graphs to explain key concepts and common challenges in evaluating interventions like marketing campaigns:
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Very happy to see our article "How causal AI can improve your decision making" - with Quentin Gallea & Konstantinos Apostolatos - published in I by IMD, discussing how companies can use causal AI to make smarter, evidence-based decisions:
imd.org
Many companies are rushing to incorporate AI into their business models without being able to accurately gauge its benefits. Applying the principles of causal inference takes away the guesswork.
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RT @bruno_ferman: 🧵New survey paper: "Inference with Few Treated Units" .Alvarez (@lafalvarez), Ferman (@bruno_ferman) and Wüthrich. Tired….
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🔥Our paper "From homemakers to breadwinners?", with Selina Gangl, is out in the Journal of Population Economics! Our RDD suggests mandatory kindergarten increases employment among previously non-employed mothers in Switzerland, but not for other groups:
link.springer.com
Journal of Population Economics - The majority of Swiss children attend mandatory and cost-free kindergarten at age four. We examine the effect of this policy on maternal labour market outcomes....
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