Fetching the paper…
Reading the bibliography…
We consider regression in which one predicts a response $Y$ with a set of predictors $X$ across different experiments or environments.
Correlation and causation
S. Wright · 1921
Earlier work this paper cites.
The probability approach in econometrics
T. Haavelmo · 1944
Earlier work this paper cites.
An inverse matrix adjustment arising in discriminant analysis
M. S. Bartlett · 1951
Earlier work this paper cites.
Tests of equality between sets of coefficients in two linear regressions
G. C. Chow · 1960
Earlier work this paper cites.
Estimating the dimension of a model
G. Schwarz · 1978
Earlier work this paper cites.
Autonomy
J. Aldrich · 1989
Earlier work this paper cites.
The logic of causal inference
K. D. Hoover · 1990
Earlier work this paper cites.
Independence properties of directed markov fields
S. L. Lauritzen, A. P. Dawid, B. N. Larsen, and H.-G. Leimer · 1990
Earlier work this paper cites.
When networks disagree: Ensemble methods for hybrid neural networks
M. Perrone and L. Cooper · 1992
Earlier work this paper cites.
Bagging predictors
L. Breiman · 1996
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
Earlier work this paper cites.
Practical use of the information-theoretic approach
K. Burnham and D. Anderson · 1998
Earlier work this paper cites.
Bayesian model averaging: a tutorial
J. Hoeting, D. Madigan, A. Raftery, and C. Volinsky · 1999
Earlier work this paper cites.
KEGG: Kyoto encyclopedia of genes and genomes
M. Kanehisa and S. Goto · 2000
Earlier work this paper cites.
Random forests
L. Breiman · 2001
Earlier work this paper cites.
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
A. Subramanian, P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, A. Paulovich, S. L. Pomeroy, T. R. Golub, E. S. Lander, and J. P. Mesirov · 2005
Earlier work this paper cites.
Foundations of modern probability
O. Kallenberg · 2006
Earlier work this paper cites.
Increased nicotinamide nucleotide transhydrogenase levels predispose to insulin hypersecretion in a mouse strain susceptible to diabetes
K. Aston-Mourney, N. Wong, M. Kebede, S. Zraika, L. Balmer, J. M. McMahon, B. C. Fam, J. Favaloro, J. Proietto, G. Morahan, and S. Andrikopoulos · 2007
Cited alongside, same era.
Sure independence screening for ultrahigh dimensional feature space
J. Fan and J. Lv · 2008
Cited alongside, same era.
Reconstructing networks of pathways via significance analysis of their intersections
M. Francesconi, D. Remondini, N. Neretti, J. M. Sedivy, L. N. Cooper, E. Verondini, L. Milanesi, and G. Castellani · 2008
Cited alongside, same era.
Using markov blankets for causal structure learning
J.-P. Pellet and A. Elisseeff · 2008
Cited alongside, same era.
Causality: Models, Reasoning, and Inference
J. Pearl · 2009
Cited alongside, same era.
Stability selection
Causal inference using invariant prediction: identification and confidence intervals
J. Peters, P. Bühlmann, and N. Meinshausen · 2016
Later among the works it cites.
The genecards suite: From gene data mining to disease genome sequence analyses
G. Stelzer, N. Rosen, I. Plaschkes, S. Zimmerman, M. Twik, S. Fishilevich, T. I. Stein, R. Nudel, I. Lieder, Y. Mazor, S. Kaplan, D. Dahary, D. Warshawsky, Y. Guan-Golan, A. Kohn, N Rappaport, M. Safran, and D. Lancet · 2016
Later among the works it cites.
Random-projection ensemble classification
T. Cannings and R. Samworth · 2017
Later among the works it cites.
The reactome pathway knowledgebase
A. Fabregat, S. Jupe, L. Matthews, K. Sidiropoulos, M. Gillespie, P. Garapati, R. Haw, B. Jassal, F. Korninger, B. May, M. Milacic, C. D. Roca, K. Rothfels, C. Sevilla, V. Shamovsky, S. Shorser, T. Varusai, G. Viteri, J. Weiser, G. Wu, L. Stein, H. Hermjakob, and P. D’Eustachio · 2017
Later among the works it cites.
Conditional variance penalties and domain shift robustness
C. Heinze-Deml and N. Meinshausen · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. Meinshausen and P. Bühlmann · 2010
Cited alongside, same era.
Domain adaptation via transfer component analysis
S. Pan, I. Tsang, J. Kwok, and Q. Yang · 2010
Cited alongside, same era.
Uniprot knowledgebase: a hub of integrated protein data
M. Magrane and UniProt Consortium · 2011
Cited alongside, same era.
Random lasso
S. Wang, N. Bin, S. Rosset, and J. Zhu · 2011
Cited alongside, same era.
On causal and anticausal learning
B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. M. Mooij · 2012
Cited alongside, same era.
A gene ontology inferred from molecular networks
J. Dutkowski, M. Kramer, M. A. Surma, R. Balakrishnan, J. M. Cherry, N. J. Krogan, and T. Ideker · 2013
Cited alongside, same era.
Single world intervention graphs (SWIGs): A unification of the counterfactual and graphical approaches to causality
T. Richardson and J. M. Robins · 2013
Cited alongside, same era.
Elements of Causal Inference: Foundations and Learning Algorithms
J. Peters, D. Janzing, and B. Schölkopf · 2017
Later among the works it cites.
Genomic determinants of protein abundance variation in colorectal cancer cells
T. I. Roumeliotis, S. P. Williams, E. Gonçalves, C. Alsinet, M. Del Castillo Velasco-Herrera, N. Aben, F. Z. Ghavidel, M. Michaut, M. Schubert, S. Price, J. C. Wright, L. Yu, M. Yang, R. Dienstmann, J. Guinney, P. Beltrao, A. Brazma, M. Pardo, O. Stegle, D. J. Adams, L. Wessels, J. Saez-Rodriguez, U. McDermott, and J. S. Choudhary · 2017
Later among the works it cites.
Invariance, causality and robustness
P. Bühlmann · 2018
Later among the works it cites.
Invariant causal prediction for nonlinear models
C. Heinze-Deml, J. Peters, and N. Meinshausen · 2018
Later among the works it cites.
Invariant causal prediction for sequential data
N. Pfister, P. Bühlmann, and J. Peters · 2018
Later among the works it cites.
Causal transfer in machine learning
M. Rojas-Carulla, B. Schölkopf, R. Turner, and J. Peters · 2018
Later among the works it cites.
Anchor regression: heterogeneous data meets causality
D. Rothenhäusler, N. Meinshausen, P. Bühlmann, and J. Peters · 2018
Later among the works it cites.
Goodness-of-fit tests for high dimensional linear models
R. Shah and P. Bühlmann · 2018
Later among the works it cites.
Learning stable structures in kinetic systems: Benefits of a causal approach
N. Pfister, S. Bauer, and J. Peters · 2019
Closest in time.
Modulation of longevity by diet, and youthful body weight, but not by weight gain after maturity
S. Roy, M. B. Sleiman, P. Jha, E. G. Williams, J. F. Ingels, C. J. Chapman, M. S. McCarty, M. Hook, A. Sun, W. Zhao, J. Huang, S. M. Neuner, L. A. Wilmott, Shapaker T., A. Centeno, K. Mozhui, M. K. Mulligan, C. C. Kaczorowski, R. W. Read, S. Saunak, R. A. Miller, J. Auwerx, and R. W. Williams · 2019
Closest in time.
Three principles of data science: predictability, computability, and stability (PCS)
B. Yu and K. Kumbier · 2019
Closest in time.