webinar register page

Causal Impacts: Extracting Evidence of Causality from Big Data
On Thursday June 25th at noon, join Data Analytics program director Dr. Dave Sheets for a data based virtual discussion. Most people are probably at least somewhat familiar with the phrase "Correlation does not imply Causation", meaning that an observed coincidence of two events does not mean that one is a result of the other, or that they even share a common underlying cause. Statistical methods are typically based on correlation patterns, extracting evidence of causation from data requires the use of claims or hypotheses generated from knowledge of the world not within the data, but based on prior experiences or business sense. Recently developed methods allow us to carry out effective hypothesis testing using data even when the gold standard of a randomized control trial is not available. Recent published estimates of Causal Impacts (changes) due to financial events and the COVID pandemic, based on advanced predictive analytic methods, will be presented and discussed.

Jun 25, 2020 12:00 PM in Eastern Time (US and Canada)

Webinar logo
Webinar is over, you cannot register now. If you have any questions, please contact Webinar host: Graduate Admissions.