English
The power of heterogeneous large-scale data for high-dimensional causal inference
Post-edited
Auteurs : Bühlmann, Peter (Auteur de la Conférence)
CIRM (Editeur )
62Fxx - Parametric inference
62H12 - Multivariate estimation
62Pxx - Applications of statistics
CIRM (Editeur )
Résumé :
We present a novel methodology for causal inference based on an invariance principle. It exploits the advantage of heterogeneity in larger datasets, arising from different experimental conditions (i.e. an aspect of "Big Data"). Despite fundamental identifiability issues, the method comes with statistical confidence statements leading to more reliable results than alternative procedures based on graphical modeling. We also discuss applications in biology, in particular for large-scale gene knock-down experiments in yeast where computational and statistical methods have an interesting potential for prediction and prioritization of new experimental interventions.
Codes MSC : 62Fxx - Parametric inference
62H12 - Multivariate estimation
62Pxx - Applications of statistics
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de publication : 18/02/16 Date de captation : 03/02/2016 Sous collection : Research talks arXiv category : Statistics Theory Domaine : Probability & Statistics Format : MP4 (.mp4) - HD Durée : 01:02:17 Audience : Researchers Download : https://videos.cirm-math.fr/2016-02-03_Buhlmann.mp4 
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Informations sur la Rencontre
Nom de la rencontre : Thematic month on statistics - Week 1: Statistical learning / Mois thématique sur les statistiques - Semaine 1 : apprentissageOrganisateurs de la rencontre : Ghattas, Badih ; Ralaivola, Liva Dates : 01/02/16 - 05/02/16 Année de la rencontre : 2016 URL Congrès : http://conferences.cirm-math.fr/1615.html
Données de citation
DOI : 10.24350/CIRM.V.18918403Citer cette vidéo: Bühlmann, Peter (2016). The power of heterogeneous large-scale data for high-dimensional causal inference. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.18918403 URI : http://dx.doi.org/10.24350/CIRM.V.18918403 |
Bibliographie
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- [2] Hauser, A., & Buhlmann, P. (2012). Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. Journal of Machine Learning Research, 13(1), 2409-2464 - http://dl.acm.org/citation.cfm?id=2503308.2503320
- [3] Kalisch, M., Machier, M., Colombo, D., Maathuis, M.H., & Buhlmann, P. (2012). Causal inference using graphical models with the R package pcalg. Journal of Statistical Software, 47(11), 1-26 - http://dx.doi.org/10.18637/jss.v047.i11
- [4] Maathuis, M.H., Colombo, D., Kalisch, M. & Buhlmann, P (2010). Predicting causal effects in large-scale systems from observational data. Nature Methods, 7(4), 247-248 - http://dx.doi.org/10.1038/nmeth0410-247
- [5] Maathuis, M.H., Kalisch, M., & Buhlmann, P. (2009). Estimating high-dimensional intervention effects from observational data. Annals of Statistics, 37(6A), 3133-3164 - http://dx.doi.org/10.1214/09-aos685
- [6] Meinshausen, N., Hauser. A. Mooij, J., Peters, J., Versteeg, P. & Bühlmann, R. (2015). Causal inference from gene perturbation experiments: methods, software and validation. Preprint. -
- [7] Peters, J., Bühlmann, R, & Meinshausen, N. (2015). Causal inference using invariant prediction: identification and confidence intervals.
- [8] Stekhoven, D.J., Morass, I., Sveinbjornsson, G., Hennig, L, Maathuis, M.H., & Buhlmann, P (2012). Causal stability ranking. Bioinformatics, 28(21), 2819-2823 - http://dx.doi.org/10.1093/bioinformatics/bts523
