Dear colleagues,
I would like to draw your attention to call for papers for the Causal Data Science Meeting 2021:
https://causalscience.org/blog/call-for-papers-2021Causality has long been an important topic in various disciplines such as computer science, economics, social science, epidemiology, and philosophy. In recent years, however, an increasing interest emerged also in the business sector with both experimental (A/B testing, reinforcement learning, etc.) and observational causal inference methods (regression methods, instrumental variables, discontinuity designs, causal discovery, etc.) being applied more frequently by practitioners. After the overwhelming success of the first CDSM in 2020, with more than 900 registered participants, we are proud to announce this year's iteration of the Causal Data Science Meeting. This two-day online workshop will bring together academics and data scientists from industry to discuss the latest methodological advances as well as practical aspects and organizational challenges around the adoption of causal inference tools.
The workshop features invited talks and presentations of accepted papers. Topics of interest include, but are not limited, to the following:
- Applications of causal inference, e.g., in management, entrepreneurship, innovation, marketing, economics, and finance
- Causal machine learning and artificial intelligence
- Data-augmented business decision-making
- Organizational challenges & best practice examples with respect to the adoption of causal inference in industry
- Experimentation & A/B testing
- Applications of Directed Acyclic Graphs and Causal Discovery
- Econometric methods & Statistics
- (Open Source) Software for causal inference
Keynote Speakers:
Sara Magliacane (VU Amsterdam & MIT-IBM Watson AI Lab): Sara Magliacane is an assistant professor in the Informatics Institute at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab. She received her PhD at the VU Amsterdam on logics for causal inference under uncertainty in 2017, focusing on learning causal relations jointly from different experimental settings, especially in the case of latent confounders and small samples. After a year in IBM Research NY as a postdoc, she joined the MIT-IBM Watson AI Lab in 2019 as a Research Scientist, where she has been working on methods to design experiments that would allow one to learn causal relations in a sample-efficient and intervention-efficient way. Her current focus is on causality-inspired machine learning, i.e. applications of causal inference to machine learning and especially transfer learning, and formally safe reinforcement learning.
Guido Imbens (Stanford GSBE): Guido Imbens is The Applied Econometrics Professor at the Stanford Graduate School of Business and Professor of Economics in the Economics Department at Stanford University. He has held tenured positions at UCLA, UC Berkeley, and Harvard University before joining Stanford in 2012. Imbens specializes in econometrics, and in particular methods for drawing causal inferences from experimental and observational data. He has published extensively in the leading economics and statistics journals. Together with Donald Rubin he has published a book, "Causal Inference in Statistics, Social and Biomedical Sciences". Guido Imbens is a fellow of the Econometric Society, the Royal Holland Society of Sciences and Humanities, the Royal Netherlands Academy of Sciences, the American Academy of Arts and Sciences, and the American Statistical Association. He holds an honorary doctorate from the University of St. Gallen. In 2017 he received the Horace Mann medal at Brown University. Currently Imbens is Editor of Econometrica.
Workshop Date: November 15–16, 2021 (13:00–20:00 Central European Time).
Submission Deadline: September 30, 2021
Submission & Participation: Please visit the website above for submission details and the registration link (participation is free of charge)
Kind regards,
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Paul Hünermund
Assistant Professor
Copenhagen Business School
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