[Statlist] Next talk: Friday, September 26, 2014 with Eleni Sgouritsa (Max Planck Institut, Tübingen)

[Cecilia Rey]

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ETH and University of Zurich

Organisers:
Proff. P. Bühlmann - L. Held - T. Hothorn - H.R. Kuensch - M. Maathuis -
N. Meinshausen - S. van de Geer - M. Wolf

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We are glad to announce the following talk:

Friday, September 26, 2014 at 15.15h  ETH Zurich HG G 19.1
with Eleni Sgouritsa (Max Planck Institut, Tübingen) 
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Title:
Identifying confounders and telling cause from effect using latent variable models

Abstract:
Drawing causal conclusions from observed statistical dependencies is a fundamental problem. Conditional-independence based causal discovery (e.g., PC or FCI) cannot be used in case there are no observed conditional independences.  Alternative methods investigate a different set of assumptions, namely restricting the model class, e.g., additive noise models. In this talk, I will present two causal inference methods employing different kind of assumptions than the above.
The first is a method to infer the existence and identify a finite confounder attaining a small number of values. It is based on a kernel method to identify finite mixtures of nonparametric product distributions. The number of mixture components is found by embedding the joint distribution into a reproducing kernel Hilbert space. The mixture components are then recovered by clustering according to an independence criterion.
In the second part I will focus on the problem of causal inference in the two-variable case (assuming causal sufficiency). The proposed method is based on the assumption that, if X causes Y, the marginal distribution P(X) contains no information about P(Y|X). In contrast, P(Y) may contain information about P(X|Y). Consequently, semi-supervised and unsupervised learning (inferring the conditional from the marginal) should be possible in the latter but not in the former case. Accordingly, a method is proposed to decide upon the causal structure.

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This abstract is also to be found under the following link: http://stat.ethz.ch/events/research_seminar
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