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Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model.
Learning embeddings into entropic wasserstein spaces
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Independent component analysis, a new concept?
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The fundamental theorem of algebra
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Causal discovery from changes
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Optimal structure identification with greedy search
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Probabilistic conditional independence structures
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Causality
Pearl, J. (2009) · 2009
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A kernel two-sample test
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Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs
Hauser, A. and Bühlmann, P. (2012) · 2012
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2013) · 2013
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Geometry of the faithfulness assumption in causal inference
Uhler, C., Raskutti, G., Bühlmann, P., and Yu, B. (2013) · 2013
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Genome-scale crispr-mediated control of gene repression and activation
Gilbert, L. A., Horlbeck, M. A., Adamson, B., Villalta, J. E., Chen, Y., Whitehead, E. H., Guimaraes, C., Panning, B., Ploegh, H. L., Bassik, M. C., et al. (2014) · 2014
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Anchored discrete factor analysis
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Backshift: Learning causal cyclic graphs from unknown shift interventions
Rothenhäusler, D., Heinze, C., Peters, J., and Meinshausen, N. (2015) · 2015
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Learning structured output representation using deep conditional generative models
Sohn, K., Lee, H., and Yan, X. (2015) · 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) · 2016
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Methods for causal inference from gene perturbation experiments and validation
Meinshausen, N., Hauser, A., Mooij, J. M., Peters, J., Versteeg, P., and Bühlmann, P. (2016) · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
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Elements of causal inference: foundations and learning algorithms
Peters, J., Janzing, D., and Schölkopf, B. (2017) · 2017
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Permutation-based causal inference algorithms with interventions
Wang, Y., Solus, L., Yang, K. D., and Uhler, C. (2017) · 2017
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Causal structure learning
Heinze-Deml, C., Maathuis, M. H., and Meinshausen, N. (2018) · 2018
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Mapping the genetic landscape of human cells
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Umap: Uniform manifold approximation and projection for dimension reduction
McInnes, L., Healy, J., and Melville, J. (2018) · 2018
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Learning directed acyclic graph models based on sparsest permutations
Raskutti, G. and Uhler, C. (2018) · 2018
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Dags with no tears: Continuous optimization for structure learning
Zheng, X., Aragam, B., Ravikumar, P. K., and Xing, E. P. (2018) · 2018
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Triad constraints for learning causal structure of latent variables
Cai, R., Xie, F., Glymour, C., Hao, Z., and Zhang, K. (2019) · 2019
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Review of causal discovery methods based on graphical models
Glymour, C., Zhang, K., and Spirtes, P. (2019) · 2019
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Nonlinear ICA using auxiliary variables and generalized contrastive learning
Matching a desired causal state via shift interventions
Zhang, J., Squires, C., and Uhler, C. (2021) · 2021
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Contrastive learning inverts the data generating process
Zimmermann, R. S., Sharma, Y., Schneider, S., Bethge, M., and Brendel, W. (2021) · 2021
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Weakly supervised causal representation learning
Brehmer, J., Haan, P. D., Lippe, P., and Cohen, T. (2022) · 2022
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Causal machine learning: A survey and open problems
Kaddour, J., Lynch, A., Liu, Q., Kusner, M. J., and Silva, R. (2022) · 2022
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Kuipers, J. and Moffa, G. (2022) · 2022
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Hyvarinen, A., Sasaki, H., and Turner, R. (2019) · 2019
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Exploring genetic interaction manifolds constructed from rich single-cell phenotypes
Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., and Weissman, J. S. (2019) · 2019
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Sliced wasserstein generative models
Wu, J., Huang, Z., Acharya, D., Li, W., Thoma, J., Paudel, D. P., and Gool, L. V. (2019) · 2019
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Gobnilp: Learning bayesian network structure with integer programming
Cussens, J. (2020) · 2020
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Causal discovery from soft interventions with unknown targets: Characterization and learning
Jaber, A., Kocaoglu, M., Shanmugam, K., and Bareinboim, E. (2020) · 2020
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Variational autoencoders and nonlinear ICA: A unifying framework
Khemakhem, I., Kingma, D., Monti, R., and Hyvarinen, A. (2020) · 2020
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Gradient-based neural dag learning
Lachapelle, S., Brouillard, P., Deleu, T., and Lacoste-Julien, S. (2020) · 2020
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Citris: Causal identifiability from temporal intervened sequences
Lippe, P., Magliacane, S., Löwe, S., Asano, Y. M., Cohen, T., and Gavves, S. (2022) · 2022
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Weight-variant latent causal models
Liu, Y., Zhang, Z., Gong, D., Gong, M., Huang, B., Hengel, A. v. d., Zhang, K., and Shi, J. Q. (2022) · 2022
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Gears: Predicting transcriptional outcomes of novel multi-gene perturbations
Roohani, Y., Huang, K., and Leskovec, J. (2022) · 2022
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Causal structure learning: a combinatorial perspective
Squires, C. and Uhler, C. (2022) · 2022
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D’ya like dags? a survey on structure learning and causal discovery
Vowels, M. J., Camgoz, N. C., and Bowden, R. (2022) · 2022
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Identification of linear non-gaussian latent hierarchical structure
Xie, F., Huang, B., Chen, Z., He, Y., Geng, Z., and Zhang, K. (2022) · 2022
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Perturbnet predicts single-cell responses to unseen chemical and genetic perturbations
Yu, H. and Welch, J. D. (2022) · 2022
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Learning linear causal representations from interventions under general nonlinear mixing
Buchholz, S., Rajendran, G., Rosenfeld, E., Aragam, B., Schölkopf, B., and Ravikumar, P. (2023) · 2023
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Learning single-cell perturbation responses using neural optimal transport
Bunne, C., Stark, S. G., Gut, G., Del Castillo, J. S., Levesque, M., Lehmann, K.-V., Pelkmans, L., Krause, A., and Rätsch, G. (2023) · 2023
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Learning nonparametric latent causal graphs with unknown interventions
Jiang, Y. and Aragam, B. (2023) · 2023
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Learning causally disentangled representations via the principle of independent causal mechanisms
Komanduri, A., Wu, Y., Chen, F., and Wu, X. (2023) · 2023
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Identification of nonlinear latent hierarchical models
Kong, L., Huang, B., Xie, F., Xing, E., Chi, Y., and Zhang, K. (2023) · 2023
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Liang, W., Kekić, A., von Kügelgen, J., Buchholz, S., Besserve, M., Gresele, L., and Schölkopf, B. (2023) · 2023
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Markham, A., Liu, M., Aragam, B., and Solus, L. (2023) · 2023
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Linear causal disentanglement via interventions
Squires, C., Seigal, A., Bhate, S. S., and Uhler, C. (2023) · 2023
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Score-based causal representation learning with interventions
Varici, B., Acarturk, E., Shanmugam, K., Kumar, A., and Tajer, A. (2023) · 2023
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Nonparametric identifiability of causal representations from unknown interventions
von Kügelgen, J., Besserve, M., Liang, W., Gresele, L., Kekić, A., Bareinboim, E., Blei, D. M., and Schölkopf, B. (2023) · 2023
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