Seminar for Statistics

Research Seminar


Time/Place: every Friday at 3.15 pm in the Main Building of ETH, HG G 19.1

Autumn Semester 2014


Date Speaker Title Time Location
26-sep-2014 (fri)
Eleni Sgouritsa
Identifying confounders and telling cause from effect using latent variable models 15:15-16:00 HG G 19.1
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.

Eleni Sgouritsa (MPI for Intelligent Systems, Tuebingen)

10-oct-2014 (fri)
Hannes Leeb
On conditional moments of high-dimensional random vectors given lower-dimensional projections 15:15-16:00 HG G 19.1
Abstract: One of the most widely used properties of the multivariate Gaussian distribution, besides its tail behavior, is the fact that conditional means are linear and that conditional variances are constant. We here show that this property is also shared, in an approximate sense, by a large class of non-Gaussian distributions. We allow for several conditioning variables and we provide explicit non-asymptotic results,whereby we extend earlier findings of Hall and Li (1993) and Leeb (2013).

(This is joint work with Lukas Steinberger.)


Hannes Leeb (Universität Wien)

14-nov-2014 (fri)
Harrison Zhou
tba 15:15-16:00 HG G 19.1
Abstract: tba

Harrison Zhou (Yale University, New Haven, CT)

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