Fetching the paper…
Reading the bibliography…
Inferring causal structures from experimentation is a central task in many domains.
Gradient-based neural DAG learning
Lachapelle, S., Brouillard, P., Deleu, T., and Lacoste-Julien, S. (2019) · 1906
Earlier work this paper cites.
Random graphs
Gilbert, E. N. (1959) · 1959
Earlier work this paper cites.
Counting labeled acyclic digraphs
Robinson, R. (1973) · 1973
Earlier work this paper cites.
A simple algorithm to construct a consistent extension of a partially oriented graph
Dor, D. and Tarsi, M. (1992) · 1992
Earlier work this paper cites.
Learning gaussian networks
Geiger, D. and Heckerman, D. (1994) · 1994
Earlier work this paper cites.
Model selection and accounting for model uncertainty in graphical models using occam’s window
Madigan, D. and Raftery, A. E. (1994) · 1994
Earlier work this paper cites.
Eliciting prior information to enhance the predictive performance of bayesian graphical models
Madigan, D., Gavrin, J., and Raftery, A. E. (1995) · 1995
Earlier work this paper cites.
Emergence of scaling in random networks
Barabási, A.-L. and Albert, R. (1999) · 1999
Earlier work this paper cites.
Data analysis with bayesian networks: A bootstrap approach
Friedman, N., Goldszmidt, M., and Wyner, A. (1999) · 1999
Earlier work this paper cites.
Causation, prediction, and search
Spirtes, P., Glymour, C. N., Scheines, R., and Heckerman, D. (2000) · 2000
Earlier work this paper cites.
Active learning of causal bayes net structure
Murphy, K. P. (2001) · 2001
Earlier work this paper cites.
Active learning for structure in bayesian networks
Tong, S. and Koller, D. (2001) · 2001
Earlier work this paper cites.
Optimal structure identification with greedy search
Chickering, D. M. (2002) · 2002
Earlier work this paper cites.
Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
Geiger, D. and Heckerman, D. (2002) · 2002
Earlier work this paper cites.
Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks
Friedman, N. and Koller, D. (2003) · 2003
Earlier work this paper cites.
Scale-free networks in cell biology
Albert, R. (2005) · 2005
Earlier work this paper cites.
On the number of experiments sufficient and in the worst case necessary to identify all causal relations among n variables
Eberhardt, F., Glymour, C., and Scheines, R. (2005) · 2005
Earlier work this paper cites.
N-1 experiments suffice to determine the causal relations among n variables
Eberhardt, F., Glymour, C., and Scheines, R. (2006) · 2006
Earlier work this paper cites.
A linear non-gaussian acyclic model for causal discovery
Shimizu, S., Hoyer, P. O., Hyvärinen, A., Kerminen, A., and Jordan, M. (2006) · 2006
Earlier work this paper cites.
Exact bayesian structure learning from uncertain interventions
Eaton, D. and Murphy, K. (2007) · 2007
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
Hoyer, P., Janzing, D., Mooij, J. M., Peters, J., and Schölkopf, B. (2008) · 2008
Earlier work this paper cites.
Causality
Pearl, J. (2009) · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y. (2010) · 2010
Earlier work this paper cites.
An introduction to Gaussian Bayesian networks
Grzegorczyk, M. (2010) · 2010
Earlier work this paper cites.
GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods
Schaffter, T., Marbach, D., and Floreano, D. (2011) · 2011
Earlier work this paper cites.
Bayesian Learning via Stochastic Gradient Langevin Dynamics
Welling, M. and Teh, Y. W. (2011) · 2011
Cited alongside, same era.
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs
Hauser, A. and Bühlmann, P. (2012) · 2012
Cited alongside, same era.
On causal and anticausal learning
Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. M. (2012) · 2012
Cited alongside, same era.
Experiment selection for causal discovery
Hyttinen, A., Eberhardt, F., and Hoyer, P. O. (2013) · 2013
Cited alongside, same era.
Stochastic Gradient Hamiltonian Monte Carlo
Chen, T., Fox, E., and Guestrin, C. (2014) · 2014
Cited alongside, same era.
Jointly interventional and observational data: estimation of interventional markov equivalence classes of directed acyclic graphs
Hauser, A. and Bühlmann, P. (2014) · 2014
Generalized score functions for causal discovery
Huang, B., Zhang, K., Lin, Y., Schölkopf, B., and Glymour, C. (2018) · 2018
Later among the works it cites.
Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties
Ouma, W. Z., Pogacar, K., and Grotewold, E. (2018) · 2018
Later among the works it cites.
Characterizing and learning equivalence classes of causal dags under interventions
Yang, K., Katcoff, A., and Uhler, C. (2018) · 2018
Later among the works it cites.
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Zheng, X., Aragam, B., Ravikumar, P., and Xing, E. P. (2018) · 2018
Later among the works it cites.
ABCD-Strategy: Budgeted experimental design for targeted causal structure discovery
Agrawal, R., Squires, C., Yang, K., Shanmugam, K., and Uhler, C. (2019) · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Addendum on the scoring of gaussian directed acyclic graphical models
Kuipers, J., Moffa, G., and Heckerman, D. (2014) · 2014
Cited alongside, same era.
Identifiability of Gaussian structural equation models with equal error variances
Peters, J. and Bühlmann, P. (2014) · 2014
Cited alongside, same era.
Structural intervention distance for evaluating causal graphs
Peters, J. and Bühlmann, P. (2015) · 2015
Cited alongside, same era.
Reconstructing causal biological networks through active learning
Cho, H., Berger, B., and Peng, J. (2016) · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B. (2016) · 2016
Cited alongside, same era.
Stein variational gradient descent: A general purpose bayesian inference algorithm
Liu, Q. and Wang, D. (2016) · 2016
Cited alongside, same era.
Kalainathan, D. and Goudet, O. (2019) · 2019
Later among the works it cites.
Learning neural causal models from unknown interventions
Ke, N. R., Bilaniuk, O., Goyal, A., Bauer, S., Larochelle, H., Schölkopf, B., Mozer, M. C., Pal, C., and Bengio, Y. (2019) · 2019
Later among the works it cites.
DAG-GNN: DAG structure learning with graph neural networks
Yu, Y., Chen, J., Gao, T., and Yu, M. (2019) · 2019
Later among the works it cites.
Differentiable causal discovery from interventional data
Brouillard, P., Lachapelle, S., Lacoste, A., Lacoste-Julien, S., and Drouin, A. (2020) · 2020
Later among the works it cites.
SERGIO: a single-cell expression simulator guided by gene regulatory networks
Dibaeinia, P. and Sinha, S. (2020) · 2020
Later among the works it cites.
Causal Discovery from Heterogeneous/Nonstationary Data
Huang, B., Zhang, K., Zhang, J., Ramsey, J., Sanchez-Romero, R., Glymour, C., and Schölkopf, B. (2020) · 2020
Later among the works it cites.
Causal discovery from soft interventions with unknown targets: Characterization and learning
Jaber, A., Kocaoglu, M., Shanmugam, K., and Bareinboim, E. (2020) · 2020
Later among the works it cites.
Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action
McFarland, J. M., Paolella, B. R., Warren, A., Geiger-Schuller, K., Shibue, T., Rothberg, M., Kuksenko, O., Colgan, W. N., Jones, A., Chambers, E., et al. (2020) · 2020
Later among the works it cites.
Permutation-Based Causal Structure Learning with Unknown Intervention Targets
Squires, C., Wang, Y., and Uhler, C. (2020) · 2020
Later among the works it cites.
Massively multiplex chemical transcriptomics at single-cell resolution
Srivatsan, S. R., McFaline-Figueroa, J. L., Ramani, V., Saunders, L., Cao, J., Packer, J., Pliner, H. A., Jackson, D. L., Daza, R. M., Christiansen, L., et al. (2020) · 2020
Later among the works it cites.
Learning sparse nonparametric DAGs
Zheng, X., Dan, C., Aragam, B., Ravikumar, P., and Xing, E. (2020) · 2020
Later among the works it cites.
Variational causal networks: Approximate bayesian inference over causal structures
Annadani, Y., Rothfuss, J., Lacoste, A., Scherrer, N., Goyal, A., Bengio, Y., and Bauer, S. (2021) · 2021
Later among the works it cites.
Bcd nets: Scalable variational approaches for bayesian causal discovery
Cundy, C., Grover, A., and Ermon, S. (2021) · 2021
Later among the works it cites.
DiBS: Differentiable Bayesian Structure Learning
Lorch, L., Rothfuss, J., Schölkopf, B., and Krause, A. (2021) · 2021
Later among the works it cites.
Beware of the simulated dag! causal discovery benchmarks may be easy to game
Reisach, A., Seiler, C., and Weichwald, S. (2021) · 2021
Later among the works it cites.
Learning neural causal models with active interventions
Scherrer, N., Bilaniuk, O., Annadani, Y., Goyal, A., Schwab, P., Schölkopf, B., Mozer, M. C., Bengio, Y., Bauer, S., and Ke, N. R. (2021) · 2021
Later among the works it cites.
Toward causal representation learning
Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., and Bengio, Y. (2021) · 2021
Later among the works it cites.
Differentiable causal discovery under latent interventions
Faria, G. R., Martins, A. F., and Figueiredo, M. A. (2022) · 2022
Closest in time.
The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions
Kuipers, J. and Moffa, G. (2022) · 2022
Closest in time.
Efficient causal structure learning from multiple interventional datasets with unknown targets
Wang, Y., Cao, F., Yu, K., and Liang, J. (2022) · 2022
Closest in time.