📢 Registration is open for the Fribourg Winter School in Data Analytics & Machine Learning (1-12 Feb 2027). Learn predictive & causal analytics with Python, R & KNIME. New this year: a course on #AgenticAI. Hybrid format: either in Fribourg or online! unifr.ch/appecon/en/wintersc…

📦 Very happy to announce a new release of our causalweight R package for causal inference+machine learning! New: continuous treatments, mediation with DiD, direct/indirect quantile effects, learning instruments & controls, effect homogeneity tests. CRAN.R-project.org/package=c…

📚 Finally, the lecture slides for my book Impact Evaluation in Firms and Organizations, published by @mitpress, are here! They can be downloaded from unifr.ch/appecon/en/research… Many thanks to K. Lötscher, S. Oberhänsli, and A. Stoller for their great work in preparing the slides!

😀 I am pleased to share that our paper “Machine learning for detecting collusion and capacity withholding in wholesale electricity markets,” joint work with Ivan Marin and Jeremy Proz, has been published open access in Energy Economics: doi.org/10.1016/j.eneco.2026… #MachineLearning

My lecture on Causal Machine Learning (discussing causal forests and double machine learning to control for confounders and assess treatment effect heterogeneity), delivered as part of the Luxembourg Institute of Health’s Lecture Series, is now online: 🔗 lih.lu/en/event/lecture-seri…

🎉My book Impact Evaluation in Firms and Organizations turns one! mitpress.mit.edu/97802625529… Impact evaluation/causal inference for business applications in a non-technical way. R/Python code & datasets freely available: unifr.ch/appecon/en/research… 📚Lecture slides coming soon @mitpress

📖 Nearly 3 years after the publication of Causal Analysis (@mitpress), I'd like to highlight the lecture slides accompanying the book. Available here as PDF and editable TeX files, together with datasets & code in R, Python, & Stata: dataverse.harvard.edu/datase…

Had the pleasure of teaching a PhD course on Causal Inference at my alma mater, the University of Innsbruck, this week. Thank you to all the participants for the engaging discussions-in the stunning heart of the Alps! 🏔️

🚀Registration is open for the #Fribourg #WinterSchool in #DataAnalytics & #MachineLearning, Feb 1–12 2027 Hybrid: at @unifr or online Topics: data analytics, predictive/causal machine learning, deep learning, agentic AI Software: Python, R, Knime Sign up: unifr.ch/appecon/en/wintersc…

🎉 Our paper, joint with Yu-Chin Hsu and Yu-Min Yen, is now published open access in JBES. We develop a double machine learning approach to estimate direct and indirect quantile treatment effects, with an application to the National Job Corps Study: doi.org/10.1080/07350015.202…

📖 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 🔗 unifr.ch/appecon/en/research… A big thank you to Sarina Oberhänsli for translating the code from R to Stata! 🙏

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 overcharges: 45%+.

🔥New working paper out (with Hannes Wallimann & Cédric Brütsch): arxiv.org/abs/2606.24181 Using 21k+ bus inspections, 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/…

🎉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: 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 levels (e.g., DiD) are (or 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/…

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

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

😀 Happy to share our new working paper with Andreas Stoller on cigarette prices, taxes, smoking: doi.org/10.48550/arXiv.2604.… Using a flexible DiD approach with double machine learning and Eurobarometer data, we find that tax increases reduce smoking, particularly among the young.