Canisius College Presents Live Webinar on “Causal Impacts: Extracting Evidence of Causality from Big Data”

June 19, 2020

Buffalo, NY – The Canisius College School of Arts & Sciences will present a live webinar on Thursday, June 25 at 12:00 p.m. titled “Causal Impacts: Extracting Evidence of Causality from Big Data.”  The webinar is being hosted by H. David Sheets, PhD, director of the MS program in data analytics at Canisius.  Because the webinar is live, participants will have the opportunity to ask questions.    

To register for the webinar, click here

Considered ‘the next frontier for innovation,’ data analytics is becoming a necessity for U.S. businesses, which consider it a key tool for investigating complex issues, identifying and solving problems, better decision-making and providing an edge over competitors.  The virtual discussion being hosted by Sheets will focus, specifically, on the familiar phrase “correlation does not imply causation,” implying 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 correlations 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.  However, new development methods enable experts to carry out effective hypothesis testing using data even when the gold standards of a randomized control trial is not available.  

Recently published estimates of causal impacts (i.e. changes) due to financial events and the COVID-19 pandemic, based on advanced predictive analytic methods, will be presented and discussed.

A veteran professor of the Physics Department, Sheets became director of the college’s one-year MS program in data analytics when it was introduced in 2018.  His tenure at Canisius includes a wide range of interdisciplinary research involving statistical and computational methods to extract information and make predictions based on large data sets. Sheets’ collaborations with scientists in entomology, anthropology, geology and forensic handwriting analysis have resulted in more than 80 publications in peer-reviewed journals.  

He holds a BS in physics from SUNY Fredonia and a PhD in physics from SUNY at Buffalo.  Aside from his faculty position, Sheets was chair of the Physics Department and director of the college’s pre-engineering program. 

The MS in data analytics at Canisius College prepares graduates to work as data specialists across a range of industries and organizations.  Students of the program learn to become adept at applying a broad range of computational and statistical methods, including exploratory and predictive analytic tools, to large data sets.  They gain a broad understanding of the principles of statistical reasoning, which will allow them to understand and assess the utility of new statistical tools as they become available and to put those tools to practical use.  Students also graduate with flexible computational skills and proficiency in at least one general purpose programming language and at least one modern statistical package.  They also study modern approaches to databases and data storage structures so they may rapidly learn new tools and packages, and maintain their own professional capabilities as new technology and procedures evolve. 

Graduates of the program are prepared for careers in a vast number of fields including market research, financial fund management, insurance and risk management, cyber security and sports analytics.  

One of 27 Jesuit universities, Canisius is the premier private university in Western New York. Canisius celebrates its sesquicentennial anniversary during the 2019-20 academic year, marking 150 years of Jesuit education and leadership in the city of Buffalo and Western New York. Visit for more information about Canisius’ milestones and celebratory events.