Our trainings are provided in collaboration with our partners from Economic AI.


Economic AI is a start-up founded by a group of leading researchers in the field of causal machine learning. The company offers solutions and software implementations for industry use cases by integrating and developing state-of-the-art researches methods of causal inference. Together with the developers of the DoubleML it offers trainings and workshops on causal machine learning. Economic AI has a network of partners, who are top-level researchers and practitioners and leaders in the field of econometrics, machine learning, mathematical statistics, and operations research. They work as professors at Harvard University, University of Hamburg, Massachusetts Institute of Technology, University of Chicago, Hong Kong University, New York University, and Duke University. The partners have extensive publication records and are winners of major academic awards and have completed projects with Microsoft, Amazon.com, State Street Corporation, Tata, DIDI, and others.


DoubleML is an open-source software for Python and R offering tools for causal machine learning. In our package, we implement the double/debiased machine learning approach by Chernozhukov et al. (2018), which offers a framework for valid statistical inference in a variety of causal models including for example (partially) linear regression models, IV models, Difference-in-Differences models and nonparametric models for estimation of heterogeneous treatment effects. For more information on the package, visit the DoubleML website and the accompanying publications in Bach et al. (2022) and Bach et al. (2021).


Bach, Philipp, Victor Chernozhukov, Sven Klaassen, Malte S. Kurz, and Martin Spindler. 2021. DoubleMLAn Object-Oriented Implementation of Double Machine Learning in R.” https://arxiv.org/abs/2103.09603.
Bach, Philipp, Victor Chernozhukov, Malte S. Kurz, and Martin Spindler. 2022. DoubleMLAn Object-Oriented Implementation of Double Machine Learning in Python.” Journal of Machine Learning Research 23 (53): 1–6. http://jmlr.org/papers/v23/21-0862.html.
Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. Double/debiased machine learning for treatment and structural parameters.” The Econometrics Journal 21 (1): C1–68. https://doi.org/10.1111/ectj.12097.