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Discovering causes and effects from observational data in the presence of hidden variables

April 4, 2013

  • Date: Thursday, April 4, 2013 
  • Time: 11:00 am — 12:30 pm 
  • Place: Mechanical Engineering 218


Subramani Mani
Assistant Professor Department of Biomedical Informatics
Vanderbilt University 

In this talk we will introduce causal Bayesian networks (CBN) and provide a working definition of causality. After a short survey of methods for learning CBNs from data we will discuss two causal discovery algorithms: the Bayesian local causal discovery algorithm (BLCD) and the post processing Y-structure algorithm (PPYA). We will present results from five simulated data sets and one real world population based data set in the medical domain. We will conclude with some potential applications in Biomedicine and research directions for the future.


Bio: Subramani Mani trained as a physician and completed his residency training in internal medicine (1990) and a research fellowship in Cardiology from the Medical College of Trivandrum, India. He then obtained a Master’s degree in Computer Science from the University of South Carolina, Columbia in 1994 and worked as a post-graduate researcher in the Department of Information and Computer Science at the University of California, Irvine. He completed his Ph.D in Intelligent Systems with a Biomedical informatics track from the University of Pittsburgh in 2005. He joined as an Assistant professor in the Department of Biomedical informatics in 2006 and was Director of the Discovery Systems Lab there before moving to the Translational Informatics Division in the Department of Internal Medicine as Associate professor in the Fall of 2012.

His research interests are data mining, machine learning, predictive modeling and knowledge discovery with a focus on discovering cause and effect relationships from observational data.