Causal Data Science Meeting 2021 – Call for Papers

Registration website

The Causal Data Science Meeting 2021 (Nov 15–16, 2021) aims to establish an interdisciplinary dialogue between academics and business experts on causal inference methods & applications.

About

Causality 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

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 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.

November 15–16, 2021 (13:00–20:00 Central European Time). The workshop will be online, jointly organized by Maastricht University and Copenhagen Business School.

Submission Deadline

September 30, 2021

October 10, 2021

Submission

Please submit your extended abstract or full paper to [email protected]. Dual submissions are permitted, i.e. your work is allowed to be previously presented or published.

Sponsorship

We are grateful for your support of the event. If you are interested to sponsor us, please contact us here. Sponsors of the event will be displayed on the conference website, with an opportunity to provide further information and job postings in the field of causal data science.