[Statlist] Next talk: Thursday, June 16, 2011 with Jonas Peters

[Cecilia Rey]

ETH and University of Zurich

Proff. P. Buehlmann -  R. Furrer - L. Held - H.R. Kuensch -
M. Maathuis -  S. van de Geer - M. Wolf


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We are glad to announce the following talk
Thursday, June 16, 2011, 15.15 HG G 19.1

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  by Jonas Peters (Max-Planck-Campus, Tübingen)

  Titel:
Causal Inference using Identifiable Functional Model Classes

Abstract:
This work addresses the following question:
Under what assumptions on the data generating process can one infer  
the causal graph from the joint distribution?
Constraint-based methods like the PC algorithm assume the Markov  
condition and faithfulness. These two conditions relate conditional  
independences and the graph structure, which allows to infer  
properties of the graph from conditional independences that can be  
found in the joint distribution. These methods, however, encounter the  
following difficulties: (1) One can discover causal structures only up  
to Markov equivalence classes, in particular one cannot distinguish  
between X -> Y and Y -> X. (2) Conditional independence testing is  
very difficult in practice. (3) When the process is not faithful, the  
results may be wrong, but the user does not realize it. We propose an  
alternative by defining dentifiable Functional Model classes (IFMOCs)  
and provide the example of additive noise models with additional  
constraints (e.g. X3=f(X1,X2)+N, but N should not be Gaussian when f  
is linear). Based on these classes we develop a causal inference  
method that overcomes some of the difficulties from before: (1) One  
can identify causal relationships even within an equivalence class.  
(2)Intuitively, fitting the model is in a sense easier than  
conditional independence testing. (3) We do not require faithfulness,  
but rather impose a model class on the data. When the model  
assumptions are violated, however, (e.g. the data do not follow the  
considered IFMOC or some of the variables are unobserved), the method  
would output "I do not know" rather than giving wrong answers.We  
regard our work as being theoretical. Although results on simulated  
data and on some real world data sets look promising, extensive  
experiments on real systems are necessary to verify the proposed  
principles.

The abstract is also to be found here:  http://stat.ethz.ch/events/research_seminar

-
ETH Zürich
Seminar für Statistik
Cecilia Rey-Lutz, HG G10.3
Rämistrasse 101
CH-8092 Zurich		                      	
mail: rey at stat.math.ethz.ch    	  		
phone: +41 44 632 3438/fax: +41 44 632 1228


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