Data Challenge
The Fourth Annual Atlantic Causal Inference Conference (ACIC) Data Challenge provides an opportunity to compare causal inference methodologies across a variety of data generating processes (DGP).
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As in previous years, the challenge focuses on computational methods of inferring causal effects from quasi-real world data. This year鈥檚 target of estimation is the population average treatment effect (ATE). There will be two tracks:
- Low dimensional datasets (varying size, e.g., 500 x 20)
- High dimensional datasets (varying size, e.g. 1000 x 200, 2000 x 200)
Participants can download datasets (between 2000 and 3000 datasets in each track), run analyses using their own computing resources, and upload results to the Challenge website for evaluation.
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The deadline for submitting results is April 15, 2019.
Timeline
The 2019 Data Challenge is now open. Preliminary results will be announced during the conference.
Key Dates
- mid-December, 2018: The Challenge website goes live. Sample datasets that can be used to develop your approach will be available for download.
- mid-January, 2019: Challenge Datasets available for download
- mid-April, 2019: Deadline for results files to be uploaded
Organizing Committee
Susan Gruber, Putnam Data Sciences, LLC
Genevi猫ve Lefebvre, Universit茅 du Qu茅bec 脿 Montr茅al
Tibor Schuster, McGill University
Alexandre Piche, MILA, Universit茅 de Montr茅al, Element AI
For more Information, contact sgruber [at] putnamds.com (subject: Data%20Challenge%20ACIC%202019) (Susan Gruber)
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