causalEDM2020: Causal Inference in EDM Fully Virtual Ifrane, Morocco, July 10, 2020 |
Conference website | https://sites.google.com/utexas.edu/causalinferenceworkshopedm2020/home |
Submission link | https://easychair.org/conferences/?conf=causaledm2020 |
Submission deadline | June 15, 2020 |
This workshop will be part of EDM 2020: the 13th International Conference on Educational Data Mining, July 10-13, a Fully Virtual Conference
General Description
Causal questions--what works, for whom, when, and why--are central to learning sciences and policy, and the the interface between causal inference and the data and methods of EDM is an exciting, crucial, under-explored area of research.
This workshop is intended to raise awareness of the ubiquity and importance of causal questions in EDM, some of the exciting methods available to address those questions, and some of the open questions of causal inference in EDM. It will include invited discussions of ongoing projects addressing causal questions, and short talks about relevant work in progress, including work in any stage of development.
Submission Guidelines
All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:
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Long (7-10 pages, 20 minute talk) talks describing EDM research with a causal flavor
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Short (4-6 pages, 10 minute) talks describing causal EDM work in progress
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Open problems (1-3 pages): In this portion of the workshop, you will briefly (~5 minutes) present a causal EDM question that you don't know how to solve. Each presentation will be followed by an open-ended discussion among the workshop participants, hopefully suggesting ways to solve, or at least better refine the problem. This sub-workshop will hopefully give the presenting researchers constructive suggestions, and spur collaborations.
List of Topics
Areas of interest include, but are not limited to:
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A/B Testing and randomized experiments
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Multi-armed bandits
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Graphical causal models/Bayesian networks
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Algorithmic causal discovery
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Causal mechanism/mediation analysis/principal stratification
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Estimating EDM program impacts
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Identifying and predicting differential effects
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Connections between machine learning and causal inference
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Dynamic treatment regimes
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Observational studies: causal inference without randomization
Organizers
Adam Sales (University of Texas, Austin/Worcester Polytechnic Institute)
Stephen Fancsali (Carnegie Learning)
Anthony Botelho (Worcester Polytechnic Institute)
Joseph Jay Williams (University of Toronto)
Neil Heffernan (Worcester Polytechnic Institute)
Venue
Fully virtual. See http://educationaldatamining.org/edm2020
Contact
All questions about submissions should be emailed to Adam Sales (asales@utexas.edu)