🚀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: https://t.co/aGUYUoOjtr

Our Fribourg Winter School in Data Analytics & Machine Learning is only a few weeks away, Feb 2–13 2026. Strengthen your skills in predictive/causal machine learning, deep learning using Python, R, Julia, Knime. Register here to join us in person or online

Very honoured to be recognised as a Distinguished Author of the Journal of Applied Econometrics in 2025 (for the equivalent of three single-authored publications). I’m grateful to my coauthors - most of my work in this journal has been collaborative! 😊

🚀 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: https://t.co/8kAqV7lqnm #CausalInference #CausalAnalysis #MachineLearning #rstats

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!

The #CDSM2025 is coming up tomorrow: https://t.co/2MI6S30LoJ. Mara Mattes will present our joint work with Jannis Kueck on learning and testing the structure of interference in social networks - how the treatment of others affects one’s own outcomes - using graph autoencoders.#AI

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

Super recomendado el libro de @CausalHuber..sencillo de leer y con la matemática justa para entender todo.

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 the organising team!

@mitpress 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. 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: 👉 https://t.co/VfahR3aqVR

😀 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: https://t.co/TxOCLwZtqE

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:

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: https://t.co/EmOCk5wi84

🎉 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! https://t.co/7l3CrweNem

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): https://t.co/qtc3KF9juJ

📘 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: https://t.co/7l3CrweNem

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:

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: https://t.co/7l3CrweNem

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