Doris Entner
Contact Information
Department of Computer Science P.O. box 68 (Gustaf Hällströmin katu 2b)
FI-00014 University of Helsinki
Finland
E-Mail: firstname.lastname@helsinki.fi
Phone: +358 9 191 51258
Office: Room A348 (Exactum)
Research
I am a PhD student, supervised by Patrik Hoyer, at the
Helsinki Institute for Information Technology (HIIT)
in the Neuroinformatics research group. My research interests include graphical models, causal discovery, and time series.
Publications
D. Entner, P.O. Hoyer, and P. Spirtes (in press)Data-driven covariate selection for nonparametric estimation of causal effects [link to code package and supp. material]
(Introduces two simple rules based on statistical dependencies and independencies to select a set of covariates for adjustment to obtain a consistent estimator of a causal effect.)
D. Entner, and P.O. Hoyer (2012)
Estimating a Causal Order among Groups of Variables in Linear Models [pdf] [link to code package and Online Appendix]
Proceedings of the 22nd International Conference on Artificial Neural Networks (ICANN-2012), Lausanne, Switzerland (Springer, LNCS vol. 7553, pp. 83-90)
Presented at the UAI Workshop on Causal Structure Learning 2012, Catalina Island, California
(Introduces a set of methods to infer a causal order among multi-dimensional vectors of random variables in linear models.)
(The original publication is available at www.springerlink.com.)
A. Moneta, D. Entner, P.O. Hoyer, and A. Coad (in press)
Causal Inference by Independent Component Analysis: Theory and Applications [link to code package]
Oxford Bulletin of Economics and Statistics. [link to online version of OBES]
(Introduces a method for SVAR identification using ICA to the econometrics community and shows how to apply it to economic data.)
D. Entner, P.O. Hoyer, and P. Spirtes (2012)
Statistical test for consistent estimation of causal effects in linear non-Gaussian models [pdf] [link to code package and supp. material]
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-2012), La Palma, Canary Islands (JMLR Workshop and Conference Proceedings 22:364-372)
(Introduces a statistical test for linear non-Gaussian acyclic models with hidden variables to infer whether an estimator for a causal effect is consistent.)
D. Entner, and P.O. Hoyer (2011)
Discovering Unconfounded Causal Relationships Using Linear Non-Gaussian Models [pdf] [link to code package]
New Frontiers in Artificial Intelligence: JSAI-isAI 2010 Workshops, Tokyo, Japan (Springer LNAI vol. 6797, pp. 181-195)
(Presents an algorithm for Linear Non-Gaussian Acyclic Models with hidden variables to discover pairwise unconfounded total effects.)
(The original publication is available at www.springerlink.com.)
A. Moneta, N. Chlass, D. Entner, and P.O. Hoyer (2011)
Causal Search in Structural Vector Autoregressive Models. [pdf]
NIPS Mini-Symposium on Causality in Time Series (JMLR Workshop and Conference Proceedings 12:95-118)
(Reviews a class of methods for causal inference in SVAR models.)
D. Entner, and P.O. Hoyer (2010)
On causal discovery from time series data using FCI [pdf] [link to code package]
Proceedings of the 5th European Workshop on Probabilistic Graphical Models (PGM-2010), Helsinki, Finland (pp. 121-128)
(Adapts the FCI algorithm of Spirtes et al. (2000) to learn causal relationships from time series data in the presence of confounding variables.)
Teaching
Autumn 2012 (second period) Teaching assistent, Introduction to Machine Learning
Autumn 2011 (second period)
Teaching assistent, Introduction to Machine Learning
Autumn 2010 (second period)
Teaching assistent, Introduction to Machine Learning
Spring 2010 (fourth period)
Teaching assistent, Unsupervised Machine Learning