Skip to main content

COVID-19 Research Resources
A curated list of research resources around guidelines, policies, and procedures related to COVID-1, drawn from Harvard University, affiliated academic healthcare centers, and government funding agencies

COVID-19 Research Resources
A curated list of research resources around guidelines, policies, and procedures related to COVID-1, drawn from Harvard University, affiliated academic healthcare centers, and government funding agencies

Calendar

Biostatistics journal club: The Algebra of Causality – May 15

Wednesday, May 15, 2019
1:00 pm – 2:00 pm
Harvard T.H. Chan School of Public Health, Building 2, Conference Room 426 (4th Fl)

Journal Club: The Algebra of Causality

Joseph Locascio, PhD, of Massachusetts General Hospital, will discuss basic data analysis methods for causal modeling. The discussion will examine how causal models can be seen as underlying research questions tested by varied statistical techniques and potential ways of making data analyses decisions given the research questions and available data.

Registration is not required.

Abstract
I would like to discuss basic data analysis methods for causal modeling. Given my background originating in applied statistics for the behavioral sciences, where causal modeling of one form or another has been used for decades and given its more recent introduction to bio-medical statistics, I would like to introduce some basic aspects of it that may still be unfamiliar to many biostatisticians. As a backdrop, I will refer to a recent book which has created quite a bit of interest on this subject, “The Book of Why” (2018) by Judea Pearl, who claims that a “Revolution in Causality” is taking place in data analysis and science. (Reading the book is not a prerequisite for my talk.) I want to emphasize that the purpose of my presentation is not to explain how to conduct any specific data analysis technique like structural equation modeling (SEM), path analysis, or confirmatory factor analysis (using e.g., the SAS Calis Procedure), though I will touch on that. Rather I will try to show people how causal models, especially as explicated with causal diagrams, can be seen as underlying research questions tested by many varied statistical techniques and to hopefully provide a helpful way of making certain decisions about appropriate data analyses given the research questions and available data. Thus, I want to emphasize causal modeling as a methodology rather than a specific method.

References
Pearl, J. The Book of Why: The New Science of Cause and Effect, co-authored with Dana Mackenzie, Basic Books, NY,NY, 2018.
Pearl, J. Mind over Data. (Excerpt from: “The Book of Why: The New Science of Cause and Effect” co-authored with Dana Mackenzie, 2018), Significance Magazine, August, 2018, Vol 15, Issue 4, pgs. 6-7.
Pearl, J. Turing award winner, longtime ASA member publishes ‘The Book of Why.’ (Interview with ASA Executive Director, Ron Wasserstein). AMSTAT News, August, 2018, Issue 494, pgs. 12-14.

Download presentation slides

Sign up to receive our newsletter: courses, funding, events, and resources.