Because correlation only gets you part of the way there
The BD2K Center for Causal Discovery, a collaboration among the University of Pittsburgh (Pitt), Carnegie Mellon University (CMU), Pittsburgh Supercomputing Center, and Yale University, proposes to hold a Datathon designed to instruct and challenge biomedical researchers on the use and application of causal modeling and discovery (CMD) tools in a “bring your own data event”.
We will invite participants to bring their own data to the event and offer cash prizes for the best analyses and results. As a prerequisite, Datathon participants will be expected to download CCD software and perform preliminary data formatting to accommodate the time frame available. We will provide the formatting specifications for their data so that they can prepare their data in advance. Participants will be required to use at least one of our CCD tools for their analysis: causal web application, causal command application, Tetrad Desktop, or causal apis (Java, R, Python).
$500 in prizes
$250 - 1st Prize
The University of Pittsburgh Innovation Institute has graciously provided a total of $500 in prize money for the datathon.
$150 - 2nd Prize
$100 - 3rd Prize
Submitting to this hackathon could earn you:
Scientists in the fields of clinical informatics, bioinformatics, and general data science as well as diverse biomedical and clinical research disciplines are invited to our datathon. As a prerequisite, participants should have taken our Summer Short Course in Causal Discovery from Biomedical Data. If you haven't take the short course, you're in luck, the annual short course immediately preceeds the datathon! See here for information about the short course. Participants for the datathon should register at the short course website as it is a prerequisite (unless you have attended previously)
Participants will prepare a short slide presentation (in person presentation optional) on the results of their analysis so that their entry can be reviewed by our panel of judges.
Clark Glymour, Ph.D.
Alumni University Professor in the Department of Philosophy at Carnegie Mellon University
Takis Benos, Ph.D.
Professor, Department of Computational and Systems Biology, University of PIttsburgh
Size and complexity of data (10 points)
Impact with regards to the causal hypotheses generated (10 points)
Innovation in the use of CCD tools (5 points)