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march, 2018

06mar1:00 pm2:00 pmPath Analysis: The Algebra of Causality1:00 pm - 2:00 pm BTM - 9004 Lecturer: Joseph Locascio Event Category:Invited talk,Statistics and machine learning

Event Details

Path Analysis (also referred to as Structural Equation Modeling, SEM, or causal modeling) is a method for systematically studying the possible causal models that may underlie an observed correlation matrix of variables. In path analysis, various explicitly detailed models of causal dynamics are hypothesized. Then the fit of the observed correlation matrix to what would be the correlation matrix predicted by a given hypothetical causal model is assessed. This is done in order to obtain support and greater specification for the proposed model and rule out as statistically improbable alternative models. Path analysis can be viewed as a specific statistical analysis method or as a more general methodological approach to research, i.e., an “algebra of causality”. I will discuss both points of view, but will emphasize more the latter. Path analysis methodology allows for a more fundamental and flexible approach to understanding some statistical techniques currently considered useful and important such as mediation/moderation analysis, factor analysis, latent variable analysis, multiple regression, partial correlation, and analysis of covariance.


(Tuesday) 1:00 pm - 2:00 pm


BTM - 9004

60 Fenwood Road


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