AQFC2015

Causal Modeling and Data Analysis: How They Benefit from Each Other?

published by seadmin on Tue, 10/14/2014 - 16:41
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                 Department of Systems Engineering and Engineering Management
                              The Chinese University of Hong Kong
 
 
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Date: Wednesday, October 22, 2014 - 11:00am - 12:30pm
 
Speaker: Dr. Kun ZHANG, Max Planck Institute for Intelligent Systems, Germany
 
Title: Causal Modeling and Data Analysis: How They Benefit from Each Other?
 
Abstract:
With the rapid accumulation of huge volumes of data, causality is becoming increasingly useful in data understanding, prediction improvement under interventions, and policy making.  Recently some enlightening progression in causal discovery, which aims to find causal knowledge from purely observational data, has been made in the statistics and machine learning fields.  On the other hand, causal information has been demonstrated to be able to facilitate understanding and solving certain data analysis problems.
 
In this talk I will first discuss how three types of "independence", namely, conditional independence, independent noise, and independent mechanism, enable causal discovery. I will illustrate their differences, and compare functional causal model based causal discovery approaches against traditional constraint-based ones. Secondly, I will consider three data analysis problems--semi-supervised learning, domain adaptation, and selection bias correction--from a causal perspective, and briefly discuss why and how the underlying causal information helps to solve them.
 
Biography:
Dr. Zhang is currently a senior research scientist at Max Planck Institute for Intelligent Systems, Germany.  His main research interests include causal discovery, machine learning, and large-scale data analysis.  He, together with his colleagues, has made a series of contributions towards solving some long-standing problems in causality, such as how to distinguish cause from effect in the two-variable case and nonparametric conditional independence test.  He received the best benchmark award of the second causality challenge and co-authored the best student paper at UAI 2010, and has been the (co-)organizer of a series of workshops to foster interdisciplinary research in causality.
 
Everyone is welcome to attend the talk!
 
Venue: Room 513, William M.W. Mong Engineering Building (ERB), The Chinese University of Hong Kong.
 
The talk will be hosted by:
Prof. Helen Meng,
Department of Systems Engineering and Engineering Management,
The Chinese University of Hong Kong,
 
SEEM-5201 Website: http://seminar.se.cuhk.edu.hk
Date: 
Wednesday, October 22, 2014 - 03:00 to 04:30