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We consider the problem of learning causal DAGs in the setting where both observational and interventional data is available.
Equivalence and synthesis of causal models
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A characterization of markov equivalence classes for acyclic digraphs
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Using bayesian networks to analyze expression data
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A comparison of novel and state-of-the-art polynomial bayesian network learning algorithms
Brown, L. E., Tsamardinos, I., and Aliferis, C. F · 2005
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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
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Kernel methods for measuring independence
Gretton, Arthur, Herbrich, Ralf, Smola, Alexander, Bousquet, Olivier, and Schölkopf, Bernhard · 2005
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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
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A linear non-gaussian acyclic model for causal discovery
Shimizu, S., Hoyer, P. O., Hyvärinen, A., and Kerminen, A · 2006
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Exact bayesian structure learning from uncertain interventions
Eaton, D. and Murphy, K · 2007
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Interventions and causal inference
Eberhardt, F. and Scheines, R · 2007
Causal discovery with continuous additive noise models
Peters, J., Mooij, J.M., Janzing, D., and Schölkopf, B · 2014
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Jointly interventional and observational data: estimation of interventional markov equivalence classes of directed acyclic graphs
Hauser, A. and Bühlmann, P · 2015
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Constraint-based causal discovery from multiple interventions over overlapping variable sets
Triantafillou, S. and Tsamardinos, I · 2015
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Perturb-seq: dissecting molecular circuits with scalable single-cell rna profiling of pooled genetic screens
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al · 2016
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Beyond editing: repurposing crispr–cas9 for precision genome regulation and interrogation
Dominguez, A. A., Lim, W. A., and Qi, L. S · 2016
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Almost optimal intervention sets for causal discovery
Eberhardt, F · 2008
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Nonlinear causal discovery with additive noise models
Hoyer, P. O., Dominik, J., Mooij, J. M., Peters, J., and Schölkopf, B · 2009
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Local characterizations of causal bayesian networks
Bareinboim, E., Brito, C., and Pearl, J · 2012
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Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs
Hauser, A. and Bühlmann, P · 2012
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Constraint-based causal discovery: Conflict resolution with answer set programming
Hyttinen, A., Eberhardt, F., and Järvisalo, M · 2014
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Magliacane, S., Claassen, T., and Mooij, J. M · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
Peters, J., Bühlmann, P., and Meinshausen, N · 2016
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Invariant causal prediction for nonlinear models
Heinze-Deml, C., Peters, J., and Meinshausen, N · 2017
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Consistency guarantees for permutation-based causal inference algorithms
Solus, L., Wang, Y., Matejovicova, L., and Uhler, C · 2017
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Permutation-based causal inference algorithms with interventions
Wang, Y., Solus, L., Yang, K., and Uhler, C · 2017
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Generalized permutohedra from probabilistic graphical models
Mohammadi, F., Uhler, C., Wang, C., and Yu, J · 2018
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