Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

CausalHighDim

Financiado

Cerrado

Causal Statistical Inference from High Dimensional Data

"Statistical causal structure learning tackles the following problem: given iid observational data from a joint distribution, we estimate the underlying causal graph. This graph contains a directed arrow from each variable to its... ver más

28/02/2015

ETH Zürich

185K€

Presupuesto del proyecto: 185K€

Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2015-02-28 No tenemos la información de la convocatoria

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

ETH Zürich

0.00€ | Lider

Proyectos interesantes

BayCause

Data Science

Software

2M€ Cerrado

ROCDISCO

Data Science

Software

203K€ Cerrado

GRAPHMODE

Data Science

Software

2M€ Cerrado

TIN2013-42351-P

Data Science

Software

68K€ Cerrado

PGC2018-101643-B-I00

Data Science

Software

142K€ Cerrado

GTBB

Data Science

Software

2M€ Cerrado

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 185K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "Statistical causal structure learning tackles the following problem: given iid observational data from a joint distribution, we estimate the underlying causal graph. This graph contains a directed arrow from each variable to its direct effects and is assumed to be acyclic. We propose to develop methods and mathematical theory for high-dimensional applications, where the number of variables is much larger than the number of samples. Independence-based methods like the PC algorithm can discover causal structures only up to Markov equivalence classes, that is some arrows remain undirected. And their consistency relies on strong faithfulness, which has been shown to be a restrictive condition. We propose to exploit structural equation models (SEMs) instead. They assume each variable to be a function of its direct causes and some noise variable. For certain restrictions (e.g. non-linear functions and additive noise) we obtain full identifiability; that is, given an observational distribution, we can recover the underlying causal graph, even without requiring faithfulness. On low-dimensional data sets, SEM-based methods already outperform competing methods like PC. However, they are not applicable to high-dimensional problems yet. One of the main goals of this research proposal is to develop new SEM-based methodology for high-dimensional applications and provide a theoretical analysis. In many applications, data are often collected under different environmental conditions. It is expected that the causal dependencies of a plant's genes, for example, behave differently under stress conditions like drought. Modeling these mechanism changes and exploiting them for causal structure learning is the second main goal of the research proposal. To the best of our knowledge, there is currently no methodology available for these tasks. We will apply the developed methodology to biological systems. The research is closely linked to the interdisciplinary project ""InfectX""."

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores