Hi everyone Instats is excited to offer a 2-day seminar, Causal Machine Learning <https://instats.org/seminar/causal-machine-learning>, livestreaming October 6–7 and led by professor Melvyn Weeks from the University of Cambridge. As causality becomes a cornerstone of credible research across the social, health, and natural sciences, this workshop blends state-of-the-art machine-learning algorithms with rigorous econometric techniques to help you move beyond correlation toward genuine causal discovery. Across eight carefully crafted sessions, you will master the distinction between prediction and causation, delve into high-dimensional regularization tools such as lasso and double lasso, and gain hands-on experience with Double Selection and Double Debiased Machine Learning. Professor Weeks will also guide you through tree- and forest-based methods for treatment-effect estimation, demonstrating how classical identification strategies integrate with modern ML workflows. Through worked notebooks and sample data in Stata, R, and Python, participants will build practical pipelines, access curated reading lists, and leave equipped to deploy causal ML confidently in their own research projects. Whether you’re a PhD student, academic, or applied professional, this seminar offers a transformative opportunity to upgrade your methodological toolkit and draw more reliable conclusions from complex datasets.
Sign up today <https://instats.org/seminar/causal-machine-learning> to secure your spot, and feel free to share this opportunity with colleagues and students who might benefit! Best wishes Michael Zyphur Professor and Director Institute for Statistical and Data Science https://instats.org [[alternative HTML version deleted]] _______________________________________________ R-sig-Epi@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-epi
