Fetching the paper…
Reading the bibliography…
We propose to address the task of causal structure learning from data in a supervised manner.
Dag-gnn: Dag structure learning with graph neural networks
Yu, Y., Chen, J., Gao, T., and Yu, M. (2019) · 1904
Earlier work this paper cites.
Gradient-based neural dag learning
Lachapelle, S., Brouillard, P., Deleu, T., and Lacoste-Julien, S. (2019) · 1906
Earlier work this paper cites.
Causal discovery with reinforcement learning
Zhu, S. and Chen, Z. (2019) · 1906
Earlier work this paper cites.
Causation, prediction, and search
Spirtes, P., Glymour, C. N., Scheines, R., Heckerman, D., Meek, C., Cooper, G., and Richardson, T. (2000) · 2000
Earlier work this paper cites.
Learning equivalence classes of bayesian-network structures
Chickering, D. M. (2002) · 2002
Earlier work this paper cites.
Causal protein-signaling networks derived from multiparameter single-cell data
Sachs, K., Perez, O., Pe’er, D., Lauffenburger, D. A., and Nolan, G. P. (2005) · 2005
Earlier work this paper cites.
Causality
Pearl, J. (2009) · 2009
Cited alongside, same era.
Cam: Causal additive models, high-dimensional order search and penalized regression
Bühlmann, P., Peters, J., Ernest, J., et al. (2014) · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Cited alongside, same era.
Group equivariant convolutional networks
Cohen, T. and Welling, M. (2016) · 2016
Cited alongside, same era.
A million variables and more: the fast greedy equivalence search algorithm for learning high-dimensional graphical causal models, with an application to functional magnetic resonance images
Ramsey, J., Glymour, M., Sanchez-Romero, R., and Glymour, C. (2017) · 2017
Cited alongside, same era.
Deep sets
Deep models of interactions across sets
Hartford, J., Graham, D. R., Leyton-Brown, K., and Ravanbakhsh, S. (2018) · 2018
Later among the works it cites.
Fashionable modelling with flux
Innes, M., Saba, E., Fischer, K., Gandhi, D., Rudilosso, M. C., Joy, N. M., Karmali, T., Pal, A., and Shah, V. (2018) · 2018
Later among the works it cites.
Dags with no tears: Continuous optimization for structure learning
Zheng, X., Aragam, B., Ravikumar, P. K., and Xing, E. P. (2018) · 2018
Later among the works it cites.
Causal learning via manifold regularization
Hill, S., Oates, C. J., Blythe, D. A., and Mukherjee, S. (2019) · 2019
Later among the works it cites.
A linear non-gaussian acyclic model for causal discovery
Shimizu, S., Hoyer, P. O., Hyvärinen, A., and Kerminen, A. (2006) · 2030
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J. (2017) · 2017
Cited alongside, same era.
The randomized causation coefficient
Lopez-Paz, D., Muandet, K., and Recht, B. (2015a)
Cited in the paper.
Towards a learning theory of cause-effect inference
Lopez-Paz, D., Muandet, K., Schölkopf, B., and Tolstikhin, I. (2015b)
Cited in the paper.
Causal discovery with continuous additive noise models
Peters, J., Mooij, J. M., Janzing, D., and Schölkopf, B. (2014) · 2053
Closest in time.