Collection Thematic month on statistics - Week 1: Statistical learning / Mois thématique sur les statistiques - Semaine 1 : apprentissage

Organizer(s) Ghattas, Badih ; Ralaivola, Liva

Date(s) 01/02/2016 - 05/02/2016

linked URL http://conferences.cirm-math.fr/1615.html

2 5

Chapters

The power of heterogeneous large-scale data for high-dimensional causal inference

By Peter Bühlmann

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.

Information about the video

Date of recording 03/02/2016
Date of publication 18/02/2016
Institution CIRM
Licence CC BY NC ND
Language English
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.18918403
Cite this video Bühlmann, Peter (03/02/2016). The power of heterogeneous large-scale data for high-dimensional causal inference. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.18918403
URL https://dx.doi.org/10.24350/CIRM.V.18918403

Domain(s)

Statistics Theory

Bibliography

[1] Hauser, A., Bühlmann, P. (2015). Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphs. Journal of the Royal Statistical Society, Series B, 77(1), 291-318 - http://dx.doi.org/10.1111/rssb.12071
[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. <arXiv:1501.01332> - http://arxiv.org/abs/1501.01332v3
[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