Boosting Local Causal Discovery in High-Dimensional Expression Data

R. Tibshirani, “Regression shrinkage and selection via the Lasso,” Journal of the Royal Statistical Society, Series B , vol. 58, pp. 267–288, 1996

1996

Earlier work this paper cites.

G. Cooper, “A simple constraint-based algorithm for efficiently mining observational databases for causal relationships,” Data Mining and Knowledge Discovery , vol. 1, no. 2, pp. 203–224, 1997

1997

Earlier work this paper cites.

R. E. Schapire, Y. Freund, P. Bartlett, W. S. Lee et al. , “Boosting the margin: A new explanation for the effectiveness of voting methods,” The annals of statistics , vol. 26, no. 5, pp. 1651–1686, 1998

1998

Earlier work this paper cites.

P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction, and Search , 2nd ed. MIT Press, 2000

2000

Earlier work this paper cites.

P. Bühlmann and B. Yu, “Boosting with the l 2 loss: regression and classification,” Journal of the American Statistical Association , vol. 98, no. 462, pp. 324–339, 2003

2003

Earlier work this paper cites.

L. S. Chen, F. Emmert-Streib, and J. D. Storey, “Harnessing naturally randomized transcription to infer regulatory relationships among genes,” Genome biology , vol. 8, no. 10, p. R219, 2007

2007

Earlier work this paper cites.

J. Pearl, Causality . Cambridge university press, 2009

2009

Cited alongside, same era.

N. Meinshausen and P. Bühlmann, “Stability Selection (with discussion),” Journal of the Royal Statistical Society Series B , vol. 72, pp. 417–473, 2010

2010

Cited alongside, same era.

P. Bühlmann and S. van de Geer, Statistics for High-Dimensional Data: Methods, Theory and Applications . Springer, 2011

2011

Cited alongside, same era.

P. Kemmeren, K. Sameith, L. van de Pasch, J. Benschop, T. Lenstra, T. Margaritis, E. O’Duibhir, E. Apweiler, S. van Wageningen, C. Ko, S. van Heesch, M. Kashani, G. Ampatziadis-Michailidis, M. Brok, N. Brabers, A. Miles, D. Bouwmeester, S. van Hooff, H. van Bakel, E. Sluiters, L. Bakker, B. Snel, P. Lijnzaad, D. van Leenen, M. Groot Koerkamp, and F. Holstege, “Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors.” Cell , vol. 157, pp. 740–752, 2014

2014

Cited alongside, same era.

N. Meinshausen, A. Hauser, J. Mooij, J. Peters, P. Versteeg, and P. Bühlmann, “Methods for causal inference from gene perturbation experiments and validation,” Proceedings of the National Academy of Sciences , vol. 113, no. 27, pp. 7361–7368, 2016

2016

Later among the works it cites.

J. M. Mooij, S. Magliacane, and T. Claassen, “Joint Causal Inference from Multiple Contexts,” arXiv e-prints , p. arXiv:1611.10351, Nov 2016

Original

2016

Later among the works it cites.

J. Peters, P. Bühlmann, and N. Meinshausen, “Causal inference by using invariant prediction: identification and confidence intervals,” Journal of the Royal Statistical Society: Series B (Statistical Methodology) , vol. 78, no. 5, pp. 947–1012, 2016

2016

Later among the works it cites.

P. Forré and J. M. Mooij, “Markov properties for graphical models with cycles and latent variables,” arXiv preprint arXiv:1710.08775 , 2017

Original

2017

Later among the works it cites.