Michelle Dunn, PhD Senior Advisor for Data Science Training, Diversity and Outreach Office of the Associate Director for Data Science NIH Monday, September 12, 2016 12:00 – 1:00 PM University Club, Conference Room A Abstract: Dr. Dunn will give a retrospective of the development of data science at the NIH[…]
Ioannis Tsamardinos, PhD, Associate Professor, Department of Computer Science, University of Crete, Greece “Logic-Based Causal Discovery for Heterogeneous Datasets” Abstract: Computational Causal Discovery aims to induce causal models, causal networks, and causal relations from observational data without performing or by performing only few interventions (perbutations, manipulations) of a system.[…]
Center for Causal Discovery Distinguished Lecture in Causal Discovery University of Pittsburgh, Carnegie Mellon University, Pittsburgh Supercomputing Center and Yale University Marloes Maathuis, PhD, Associate Professor, Department of Statistics, ETH Zurich, Switzerland, “TBA,” at 9:00 am on Friday, November 11, 2016, in Rooms 407A/B BAUM, 5607 Baum Blvd., The[…]
Ricardo Silva, PhD, Lecturer, Department of Statistical Science and Centre for Computational Statistics and Machine Learning, University College London, “Learning Causal Effects: Bridging Instruments and Backdoors,” at 11:00 am on Thursday, September 15, 2016, in Rooms 407A/B BAUM, 5607 Baum Blvd., The Offices at Baum. Abstract: We consider the problem[…]
Causal Discovery Datathon – June 17-18, 2016, Carnegie Mellon University, Pittsburgh, PA Location: Baker Hall, Giant Eagle Auditorium, A51, CMU Datathon Director: Jeremy Espino, MD, MS ⇒ Registration CLOSED ⇐ Immediately following the CCD Summer Short Course, we will hold a Datathon designed to instruct and challenge biomedical researchers on the use and[…]
Causal Discovery from Biomedical Data – June 13-17, 2016, Carnegie Mellon University, Pittsburgh, PA Location: Baker Hall, Giant Eagle Auditorium, A51, CMU Course Director: Richard Scheines, PhD ⇒ Registration CLOSED for BOTH Short Course and Datathon ⇐ Attendees, either biomedical or data scientists, are expected to have knowledge of basic statistical principles, but no prior[…]
Susan Athey, PhD Stanford University This talk will cover a pair of papers that modify popular machine learning methods for application to estimating how treatment effects vary with observable unit characteristics. Applications include large scale field experiments or online A/B tests. The first paper develops ?causal trees,? a modification of[…]
There are many actions researchers can take to increase the openness and reproducibility of their work. Please join us for a workshop, hosted by the Center for Open Science, to learn easy, practical steps researchers can take to increase the reproducibility of their work. The workshop will be hands-on. Using[…]
Frederick Eberhardt, PhD, Professor of Philosophy, Cal Tech