📖 News about my book #CausalAnalysis! Many empirical examples are now also available in #Stata - in addition to #R and #Python. You can find the data and code files at 🔗 www.unifr.ch/appecon/en/r... A big thank you to Sarina Oberhänsli for translating the code from R to Stata! 🙏

Very happy to share our new working paper (with D. Imhof & T. Madiès) on cartel behavior, detection, and damages in public procurement: arxiv.org/abs/2606.30470 We show how firms coordinated bids in a Swiss bid-rigging cartel, while mimicking competition. Estimated average overcharges: at least 45%.

🔥New working paper out (with Hannes Wallimann & Cédric Brütsch): arxiv.org/abs/2606.24181 Using 21k+ bus inspections in Switzerland, we estimate the causal effect of inspector visibility. Result: uniformed inspections detect ~25% fewer fare evaders per hour than undercover inspections.

Delighted to be attending the Econometric Society African Meeting in Cairo (#AFES2026) as invited speaker. Will present joint work with M. Haddad, J. Medina-Reyes, L. Zhang on difference-in-differences for continuous treatments using machine learning: virtual.oxfordabstracts.com/event/76129/...

✅ today: econometrics, treatment effects. 15 days to prelim #econsky #econtwitter book by @causalhuber.bsky.social

🎉Our paper "Testing Monotonicity of Mean Potential Outcomes in a Continuous Treatment with High-Dimensional Data", joint work with Yu-Chin Hsu, Ying-Ying Lee, and Chu-An Liu, has been published in the Review of Economics and Statistics @restatjournal.bsky.social: direct.mit.edu/rest/article...

🆕 New working paper: When is exploiting treatment changes rather than treatment levels valid for causal inference? I develop a formal framework and characterize when strategies based on treatment changes and treatment levels (e.g., DiD) are - and are not - equivalent. arxiv.org/abs/2606.02234

🚀 Excited to share that our paper “A Joint Test of Unconfoundedness and Common Trends”, joint work with Eva-Maria Oeß has now been published in the Journal of Applied Econometrics: onlinelibrary.wiley.com/doi/10.1002/...

🔥 Very happy to share our new working paper (with M. Bia, G. Menta, and C. D’Ambrosio) on estimating local average treatment effects using genetic instruments, while allowing for some instruments to be invalid and for heterogeneity in treatment responses across instruments: docs.iza.org/dp18595.pdf

I had the pleasure of visiting IMT in Lucca (www.imtlucca.it), where I presented our recent work on combining difference-in-differences for continuous treatments with double machine learning for control variable selection (joint with M Haddad, J Medina-Reyes, and L Zhang): arxiv.org/abs/2410.21105.

😀 Happy to share our new working paper with Andreas Stoller on cigarette prices, taxes, and smoking: doi.org/10.48550/arX... Using a flexible diff-in-diff approach with double machine learning and Eurobarometer data, we find that tax increases reduce smoking, particularly among the young.

🚀 New working paper with Kevin Kloiber & @lukaslaffers.bsky.social: arxiv.org/abs/2603.04109 We propose a statistical test for full mediation of treatment effects and the identifiability of causal mechanisms, using double machine learning to control for high-dimensional covariates.

🚀 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.

🔥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...

📢 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!

📄 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...

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

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.

📢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!

📚 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...

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/...

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...

📘 My book Impact Evaluation in Firms and Organizations is officially out today with @mitpress.bsky.social! An accessible, non-technical introduction to impact evaluation (& causal machine learning) designed for practitioners & students, with use cases in R & Python: mitpress.mit.edu/978026255292...

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/abs/2507.12369 #MachineLearning

And here it is! 😀 Just received my author copies of "Impact Evaluation in Firms and Organizations", out with @mitpress.bsky.social on Aug 5. It offers a compact overview of methods for evaluating interventions, like marketing campaigns, with R and Python examples: mitpress.mit.edu/978026255292...

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! #DataScience #MachineLearning #CausalAnalysis

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

Struggling to identify causal effects when experiments aren’t feasible? Learn how Instrumental Variables and Regression Discontinuity Designs can help! Join @causalhuber.bsky.social‬ to dive deep into IV and RDD in R —from core concepts to advanced topics like machine learning: t1p.de/caus-inf-2025

*Causal Inference with Instrumental Variables and Regression Discontinuity Designs* by @causalhuber.bsky.social. 🗓️ 09 – 10 October 2025 🏢 Online 🌐 t1p.de/caus-inf-2025

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: arxiv.org/abs/2407.04448

1.5 months to go until my book Impact Evaluation in Firms and Organizations is out with @mitpress.bsky.social! An accessible introduction to impact evaluation & causal machine learning, with examples in #R & #Python: mitpress.mit.edu/978026255292... Examples: dataverse.harvard.edu/dataset.xhtm...

Great to be visiting the Chair for Entrepreneurship at the University of Zurich for a seminar on data analytics and machine learning. Many thanks to @ulrichkaiser.bsky.social and his team for the warm welcome and generous hospitality! #DataScience

Visiting the Centre d'études et de recherches sur le développement international cerdi.bsky.social in beautiful 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!

🚀 Less than 3 months to go! My book "Impact Evaluation in Firms and Organizations" (mitpress.bsky.social) is now available for pre-order: mitpress.mit.edu/978026255292... It features intuitive graphs to explain key concepts and common challenges in evaluating interventions like marketing campaigns: