Martin Huber

Difference-in-differences for mediation analysis using double machine learning We propose a difference-in-differences (DiD) framework with mediation for possibly multivalued discrete or continuous treatments and mediators, aimed at identifying the direct effect of the treatment ...

🚀 New working paper - joint with S. Oberhänsli:
arxiv.org/abs/2602.23877
We propose a DiD approach to mediation analysis that evaluates direct, indirect, & dynamic treatment effects under conditional parallel trends, using double machine learning for flexible, data-driven covariate control.

Testing Effect Homogeneity and Confounding in High-Dimensional Experimental and Observational Studies We propose a framework for testing the homogeneity of conditional average treatment effects (CATEs) across multiple experimental and observational studies. Our approach leverages multiple randomized t...

🔥New working paper (joint with A. Armendáriz): We propose a test for the homogeneity of conditional average treatment effects across experimental and observational studies, helping to disentangle unobserved confounding from effect heterogeneity in causal estimates: arxiv.org/abs/2602.19703

📣The 2026 Symposium of #CausalInference in the #HealthSciences takes place on March 18, 2026 in Fribourg. Theme: AI & machine learning in causal inference for health sciences
🎤Keynotes: Elsa Gautrain, Aurélien Sallin, Jonas Peters, Jana Mareckova
🔗https://projects.unifr.ch/pophealthlab/?page_id=1561

📢 Registration is open for the 2026 Symposium of Causal Inference in the Health Sciences, hosted at Fribourg University on March 18. This year’s focus is on AI/machine learning in causal inference for health sciences/economics - register here: projects.unifr.ch/pophealthlab... #CausalAI

📢 Last call! Register for the #Fribourg #WinterSchool in #DataAnalytics & #MachineLearning (📅 Feb 2–13, 2026) until Jan 25. Join us on site in Fribourg or online for data analytics, predictive/causal machine learning, and deep learning, using Python, R, Julia, & KNIME: www.unifr.ch/appecon/en/w...

Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning We propose a difference-in-differences (DiD) framework designed for time-varying continuous treatments across multiple periods. Specifically, we estimate the average treatment effect on the treated (A...

📢 Update of our #DiD paper on continuous treatments with machine learning-based covariate adjustment, joint with M Haddad, J Medina-Reyes, and L Zhang. Now includes an evaluation of the impact of second-dose COVID-19 vaccination rates on mortality in Brazil:
arxiv.org/abs/2410.21105

The new year starts with a great conference: the Labor Seminar in #Laax 🏔️ Inspiring talks with applications of causal inference methods in empirical labor economics and related fields. Thanks to Pia Schilling and Christina Felfe for putting together a fantastic program!

Learning and Testing Exposure Mappings of Interference using Graph Convolutional Autoencoder Interference or spillover effects arise when an individual's outcome (e.g., health) is influenced not only by their own treatment (e.g., vaccination) but also by the treatment of others, creating chal...

📄 New paper (joint with J Kueck & M Mattes):
arxiv.org/abs/2601.05728
When outcomes depend on others’ actions in a social network, causal evaluation becomes difficult. We use causal AI to learn network interference from data and to test whether common ways of modelling interference are valid.

📢The #Fribourg #WinterSchool in #DataAnalytics & #MachineLearning is coming up Feb 2-13. Join us on site at @ses_unifr or online for a two-week program on data analytics, predictive/causal machine learning & deep learning using Python, R, Julia & KNIME:
www.unifr.ch/appecon/en/w...

Happy holidays from the #Venet in #Tirol, #Austria ❄️⛷️ - the perfect crowd-free ski retreat, recharging energy for fresh #CausalAnalysis and #ImpactEvaluation in the new year 😉 www.venet.at

⏳ Our #Fribourg #WinterSchool in Data Analytics & Machine Learning is only a few weeks away (Feb 2–13, 2026). Strengthen your skills in predictive and causal machine learning, deep learning using Python, R, Julia & Knime.
Register here to join us in person or online: www.unifr.ch/appecon/en/w...

Journal of Applied Econometrics DISTINGUISHED AUTHORS ANNOUNCEMENT The Journal of Applied Econometrics is a statistical and mathematical economics journal for the application of econometric techniques to economic problems.

Very honored to be recognized as a Distinguished Author of the Journal of Applied Econometrics in 2025 (for the equivalent of three single-authored publications). I’m very grateful to my coauthors - most of my work in this journal has been collaborative! 😊 onlinelibrary.wiley.com/page/journal...

🚀 A new version of our causalweight package for the statistical software R is online, containing some of the latest causal machine learning methods for the estimation of treatment effects: www.rdocumentation.org/packages/cau...
#CausalInference #CausalAnalysis #MachineLearning

Had the great pleasure of teaching a short course on #CausalAnalysis (based on my book of the same name) and methods in policy evaluation this week at the European Central Bank in Frankfurt. A big thank you to David Marques-Ibanez and all participants for hosting me and for the engaging discussions!

‪
With @jeromevalette.bsky.social & Jesús Fernández-Huertas Moraga, we are happy to announce the CfPapers for the

4th edition of the Junior Workshop on the Economics of Migration

on May 26-27, 2026 @uc3meconomics.bsky.social, Spain.

Submit until February 1, 2026 on economig2026.sciencesconf.org

Causal Data Science Meeting - Home Fostering a dialogue between industry and academia on causal data science.

The #CDSM2025 is coming up tomorrow: www.causalscience.org. Mara Mattes will present our joint work with Jannis Kueck on learning and testing the structure of interference effects in social networks - how the treatment of others affects one’s own outcomes - using graph convolutional autoencoders.

@eckhoffandresen.bsky.social @andreassteinmayr.net @cdechaisemartin.bsky.social @causalinf.bsky.social @gaborbekes.bsky.social @p-hunermund.com @essobecker.bsky.social @ho2604.bsky.social @chtraxler.bsky.social @ulrichkaiser.bsky.social @vfsecon.bsky.social @zeileis.org @izmartinez86.bsky.social

📢The #Fribourg #WinterSchool in #DataAnalytics & #MachineLearning is coming up (Feb 2–13, 2026)! On site at @unifr.bsky.social or online - covering data analytics, predictive & causal machine learning, and deep learning using Python, R, Julia & Knime. Register now: www.unifr.ch/appecon/en/w...

Very happy to attend the Young Researcher Workshop of the Universities of Tübingen and #Hohenheim (as an invitee, even if I’m not that young anymore 😉) - lots of great presentations and lively discussions, including on causal machine learning! Many thanks to B. Jung, M. Biewen & the organising team!

Huge thanks to Emma Bacci, Sarina J. Oberhänsli, Jeremy Proz, Andreas Stoller, and Melissa Uhrig for their great work and support in preparing the teaching slides!

📚 In summer 2023, my book Causal Analysis was published with @mitpress.bsky.social. Just two years later😉 I’m very happy to share that the lecture slides are now freely available in both PDF and LaTeX (as zip files), along with the datasets and R/Python code:
👉 www.unifr.ch/appecon/en/r...

📣 Last Call!
Don't miss the chance to 🤿 dive into IV and RDD in R with @causalhuber.bsky.social!
Register Now!
➡️ t1p.de/caus-inf-2025

🚀 Registration is open for the #Fribourg #WinterSchool in #DataAnalytics & #MachineLearning, Feb 2–13 2026.
📍Hybrid: at Fribourg University or online
🔍Topics: data analytics, predictive/causal machine learning, deep learning
💻 Software: Python, R, Julia, Knime
👉 Sign up: www.unifr.ch/appecon/en/w...

Thank you, very happy to hear that you like it!

Thanks @p-hunermund.com!

😀 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: virtual.oxfordabstracts.com/event/73643/...

Machine Learning for Detecting Collusion and Capacity Withholding in Wholesale Electricity Markets 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 to t...

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 among suppliers in electricity markets:
👉 arxiv.org/abs/2508.09885

Very happy to be teaching a @gesistraining.bsky.social workshop on causal inference with instrumental variables and regression discontinuity designs on October 9–10, 2025. Registration is still open: training.gesis.org?site=pDetail...

🎉 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!
mitpress.mit.edu/978026255292...

This moment also 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): mitpress.mit.edu/978026254591...