Fetching the paper…
Reading the bibliography…
Causal reasoning is a crucial part of science and human intelligence.
Characterization problems in mathematical statistics
A.M. Kagan, Y.V. Linnik, and C.R. Rao. 1973 · 1973
Earlier work this paper cites.
Causation
D. Lewis. 1973 · 1973
Earlier work this paper cites.
New Directions in the Theory of Graphs
R.W. Robinson. 1973 · 1973
Earlier work this paper cites.
Outline of a theory of practice
P. Bourdieu. 1977 · 1977
Earlier work this paper cites.
Testing for causality: A personal viewpoint
C.W.J. Granger. 1980 · 1980
Earlier work this paper cites.
Dynamical Systems and Turbulence, Lecture notes in Mathematics 898
1981 · 1981
Earlier work this paper cites.
The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks
I.A. Beinlich, H.J. Suermondt, R.M. Chavez, and G.F. Cooper. 1989 · 1989
Earlier work this paper cites.
Nonlinear principal component analysis using autoassociative neural networks
M.A. Kramer. 1991 · 1991
Earlier work this paper cites.
Equivalence and synthesis of causal models
T. Verma and J. Pearl. 1991 · 1991
Earlier work this paper cites.
A Bayesian method for the induction of probabilistic networks from data
G.F. Cooper and E. Herskovits. 1992 · 1992
Earlier work this paper cites.
Bayesian statistics (4th ed.)
D.J. Spiegelhalter and R.G. Cowell. 1992 · 1992
Earlier work this paper cites.
Simple statistical graident-following algorithms for connectionist reinforcement learning
R.J. Williams. 1992 · 1992
Earlier work this paper cites.
Learning Gaussian networks
D. Geiger and D. Heckerman. 1994 · 1994
Earlier work this paper cites.
Learning Bayesian belief networks: an approach based on the MDL principle
W. Lam and F. Bacchus. 1994 · 1994
Earlier work this paper cites.
A limited memory algorithm for bound constrained optimization
R.H. Byrd, P. Lu, J. Nocedal, and C. Zhu. 1995 · 1995
Earlier work this paper cites.
Learning Bayesian networks: the combination of knowledge and statistical data
D. Heckerman, D. Geiger, and D.M. Chickering. 1995 · 1995
Earlier work this paper cites.
Learning from Data. Lecture notes in statistics, vol 112
D.M. Chickering. 1996 · 1996
Earlier work this paper cites.
Value-sensitive design
B. Friedman. 1996 · 1996
Earlier work this paper cites.
The Grand Leap
P. Humphreys and D. Freedman. 1996 · 1996
Earlier work this paper cites.
A discovery algorithm for directed cyclic graphs
T. Richardson. 1996 · 1996
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber. 1997 · 1997
Earlier work this paper cites.
In search of the philosopher’s stone: Remarks on Humphreys and Freedman’s critique of causal discovery
K.B. Korb and C.S. Wallace. 1997 · 1997
Earlier work this paper cites.
Algorithm 778: L-BFGS-B Fortran subroutines for large-scale bound-constrained optimization
C. Zhu, R.H. Byrd, P. Lu, and J. Nocedal. 1997 · 1997
Earlier work this paper cites.
Emergence of scaling in random networks
A.-L Barabási and R. Albert. 1999 · 1999
Earlier work this paper cites.
Are there algorithms that discovery causal structure?
D. Freedman and P. Humphreys. 1999 · 1999
Earlier work this paper cites.
Optimization II: Numerical methods for nonlinear continuous optimization
A. Nemirovsky. 1999 · 1999
Earlier work this paper cites.
Causation, prediction, and search (2nd ed.)
P. Spirtes, C. Glymour, and R. Scheines. 2000 · 2000
Earlier work this paper cites.
Independent Component Analysis
A. Hyvärinen, J. Karhunen, and E. Oja. 2001 · 2001
Earlier work this paper cites.
Learning Bayesian networks from data: An information-theory based approach
J. Cheng, R. Greiner, J. Kelly, D. Bell, and W. Liu. 2002 · 2002
Earlier work this paper cites.
Optimal structure identification with greedy search
D.M. Chickering. 2002 · 2002
Earlier work this paper cites.
Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression
S. Imoto, T. Goto, and S. Miyano. 2002 · 2002
Earlier work this paper cites.
Ancestral graph Markov models
T. Richardson and P. Spirtes. 2002 · 2002
Earlier work this paper cites.
Being Bayesian about network structure: a Bayesian approach to structure discovery in Bayesian networks
N. Friedman and D. Koller. 2003 · 2003
Earlier work this paper cites.
Clarifying the usage of structural models for commonsense causal reasoning
M. Hopkins and J. Pearl. 2003 · 2003
Earlier work this paper cites.
Highly Structured Stochastic Systems
T.S. Richardson and P. Spirtes. 2003 · 2003
Earlier work this paper cites.
Learning causal structures based on Markov equvialence class
Y-B. He, Z. Geng, and X. Liang. 2005 · 2005
Earlier work this paper cites.
Causality and counterfactuals in the situation calculus
M. Hopkins and J. Pearl. 2005 · 2005
Earlier work this paper cites.
Causal protein-signaling networks derived from multiparameter single-cell data
K. Sacks, O. Perez, D. Pe’er, D.A. Lauffenburger, and G.P. Nolan. 2005 · 2005
Earlier work this paper cites.
Ordering-based search: A simple and effective algorithm for learning Bayesian networks
M. Teyssier and D. Koller. 2005 · 2005
Earlier work this paper cites.
Interventions and Causal Inference
F. Eberhardt and R. Scheine. 2006 · 2006
Earlier work this paper cites.
High-dimensional graphs and variable selection with the lasso
N. Meinshausen and P. Bühlmann. 2006 · 2006
Earlier work this paper cites.
Adjacency-faithfulness and conservative causal inference
J. Ramsey, J. Zhang, and P. Spirtes. 2006 · 2006
Earlier work this paper cites.
A linear non-Gaussian acyclic model for causal discovery
S. Shimizu, P.O. Hoyer, A. Hyvärinen, and A. Kerminen. 2006 · 2006
Earlier work this paper cites.
The max-min hill-climbing Bayesian network structure learning algorithm
I. Tsamardinos, L.E. Brown, and C.F. Aliferis. 2006 · 2006
Earlier work this paper cites.
SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms
T. Van den Bulcke, K. Van Leemput, B. Naudts, P. van Remortel, H. Ma, A. Verschoren, B. De Moor, and Marchal K. 2006 · 2006
Earlier work this paper cites.
Exact Bayesian structure learning from uncertain interventions
D. Eaton and K. Murphy. 2007 · 2007
Earlier work this paper cites.
A kernel method for the two-sample problem
A. Gretton, K.M. Borgwardt, M. Rasch, B. Schölkopf, and A.J. et al. Smola. 2007 · 2007
Earlier work this paper cites.
A kernel-based causal learning algorithm
X. Sun, D. Janzing, B. Schölkopf, and K. Fukumizu. 2007 · 2007
Earlier work this paper cites.
Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data
O. Banerjee, L. El Ghaoui, and A. d’Aspremont. 2008 · 2008
Earlier work this paper cites.
Beware of the DAG!
A.P. Dawid. 2008 · 2008
Earlier work this paper cites.
Learning causal Bayesian network structures from experimental data
B. Ellis and W.H. Wong. 2008 · 2008
Earlier work this paper cites.
Sparse inverse covariance estimation with the graphical lasso
J. Friedman, T. Hastie, and R. Tibshirani. 2008 · 2008
Earlier work this paper cites.
Kernel measures of conditional dependence
K. Fukumizu and A. Gretton. 2008 · 2008
Earlier work this paper cites.
Handbook of the Philosophy of Information
P.D. Grünwald and P.M. Vitányi. 2008 · 2008
Earlier work this paper cites.
Active learning of causal networks with intervention experiments and optimal designs
Y-B. He and Z. Geng. 2008 · 2008
Earlier work this paper cites.
Nonlinear causal discovery with additive noise models
P.O. Hoyer, D. Janzing, J.M. Mooij, and J. Peters. 2008a · 2008
Earlier work this paper cites.
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
P.O. Hoyer, S. Shimizu, A.J. Kerminen, and M. Palviainen. 2008b · 2008
Earlier work this paper cites.
Using Markov blankets for causal structure learning
J.P. Pellet and A. Elisseeff. 2008 · 2008
Earlier work this paper cites.
Integrating locally learned causal structures with overlapping variables
R.E. Tillman, D. Danks, and C. Glymour. 2008 · 2008
Earlier work this paper cites.
Markov equivalence for ancestral graphs
R.A. Ali, T.S. Richardson, and P. Spirtes. 2009 · 2009
Earlier work this paper cites.
A comparison of structural distance measures for causal Bayesian network models
M. de Jongh and M.J. Druzdzel. 2009 · 2009
Earlier work this paper cites.
Computing maximum likelihood estimates in recursive linear models with correlated errors
M. Drton, M. Eichler, and T.S. Richardson. 2009 · 2009
Earlier work this paper cites.
Learning bounded treewidth Bayesian networks
G. Elidan and S. Gould. 2009 · 2009
Earlier work this paper cites.
Theory-based causal induction
T.L. Griffiths and J. B. Tenenbaum. 2009 · 2009
Earlier work this paper cites.
Identifying confounders using additive noise models
D. Janzing, J. Peters, J. Mooij, and B. Scholkopf. 2009 · 2009
Earlier work this paper cites.
Probabilistic Graphical Models: Principles and Techniques
D. Koller and N. Friedman. 2009 · 2009
Earlier work this paper cites.
Estimating high-dimensional intervention effects from observational data
M.H. Maathuis, M. Kalisch, and P. Bühlmann. 2009 · 2009
Earlier work this paper cites.
Generating realistic in silico gene networks for performance assessment of reverse engineering methods
D. Marbach, T. Schaffter, C. Mattiussi, and D. Floreano. 2009 · 2009
Earlier work this paper cites.
Causality
J. Pearl. 2009 · 2009
Earlier work this paper cites.
The graph neural network model
F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, and G. Monfardini. 2009 · 2009
Earlier work this paper cites.
Causal inference using the algorithmic Markov condition
D. Janzing and B. Schölkopf. 2010 · 2010
Earlier work this paper cites.
Distinguishing cause from effect
J.M. Mooij and D. Janzing. 2010 · 2010
Earlier work this paper cites.
Penalized likelihood methods for estimation of sparse high-dimensional directed acyclic graphs
A. Shojaie and G. Michailidis. 2010 · 2010
Earlier work this paper cites.
Learning causal structure from overlapping variable sets
S. Triantafillou, I. Tsamardinos, and Tollis. I. 2010 · 2010
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G.J. Gordon, and J.A. Bagnell. 2011 · 2011
Earlier work this paper cites.
Network modelling methods for fMRI
S.M. Smith, K.L. Miller, G. Salimi-Khorshidi, M. Webster, C.F. Beckmann, T.E. Nichols, J.D. Ramsey, and M.W. Woolrich. 2011 · 2011
Earlier work this paper cites.
Targeted Learning - Causal Inference for Observational and Experimental Data
M. J. van der Laan and S. Rose. 2011 · 2011
Earlier work this paper cites.
Multi-domain sampling with applications to structural inference of Bayesian networks
Q. Zhou. 2011 · 2011
Earlier work this paper cites.
Learning high-dimensional directy acyclic graphs with latent and selection variables
D. Colombo, M.H. Maathuis, M. Kalisch, and T.S. Richardson. 2012 · 2012
Earlier work this paper cites.
One iteration CHC algorithm for learning Bayesian networks: an effective and efficient algorithm for high dimensional problems
J. Gámez, J.L. Mateo, and J. Puerta. 2012 · 2012
Earlier work this paper cites.
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs
P. Hauser, A.and Bühlmann. 2012 · 2012
Earlier work this paper cites.
Information-geometric approach to inferring causal directions
D. Janzing, J. Mooij, J. Zhang, K.and Lemeire, J. Zscheischler, P. Daniusis, B. Steudel, and B. Schölkopf. 2012 · 2012
Earlier work this paper cites.
Causal inference using graphical models with the R package pcalg
M. Kalisch, M. Mächler, D. Colombo, M.H. Maathuis, and P. Bühlmann. 2012 · 2012
Earlier work this paper cites.
Wisdom of crowds for robust gene network inference
D. Marbach, J.C. Costello, R. Küffner, N.M. Vega, R.J. Prill, D.M. Camacho, K.R. Allison, M. Kellis, J.J. Collins, and G. Stolovitzky. 2012 · 2012
Earlier work this paper cites.
Detecting causality in complex ecosystems
G. Sugihara, R. May, C.-h. Hsieh, E. Deyle, M. Fogarty, and S. Munch. 2012 · 2012
Earlier work this paper cites.
Kernel-based conditional independence test and application in causal discovery
K. Zhang, J. Peters, D. Janzing, and B. Schölkopf. 2012 · 2012
Earlier work this paper cites.
Scaling up the greedy equivalence algorithm by constraining the seach space of equivalence classes
J.I. Alonso-Barba, L. de la Ossa, J.A. Gámez, and J.M. Puerta. 2013 · 2013
Earlier work this paper cites.
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent. 2013 · 2013
Earlier work this paper cites.
Counterfactual reasoning and learning systems: the example of computational advertising
L. Bottou, J. Peters, Quinonero-Candela J., D. X. Charles, D. M. Chickering, E. Portugaly, D. Ray, P. Simard, and E. Snelson. 2013 · 2013
Earlier work this paper cites.
Causal inference with multiple time series: principles and problems
M. Eichler. 2013 · 2013
Earlier work this paper cites.
Learning sparse causal Gaussian networks with experimental intervention: regualrization and coordinate descent
F. Fu and Q. Zhou. 2013 · 2013
Earlier work this paper cites.
PC algorithm for nonparanormal graphical models
N. Harris and M. Drton. 2013 · 2013
Cited alongside, same era.
Discoverying cyclic causal models with latent variables: a genearal SAT-based procedure
A. Hyttinen, F. Hoyer, O.and Eberhardt, and M. Jarvisalo. 2013 · 2013
Cited alongside, same era.
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models
A. Hyvärinen and S.n.M. Smith. 2013 · 2013
Cited alongside, same era.
From ordinary differential equations to structural causal models: the deterministic case
J.M. Mooij, D. Janzing, and B. Schölkopf. 2013 · 2013
Cited alongside, same era.
Black box variational inference
R. Ranganath, S. Gerrish, and D.M. Blei. 2013 · 2013
Cited alongside, same era.
Single World Intervention Graphs: a primer
Generative neural networks to infer causal mechanisms: algorithms and applications
D. Kalainathan. 2019 · 2019
Later among the works it cites.
Causal discovery toolbox: uncover causal relationships in Python
D. Kalainathan and O. Goudet. 2019 · 2019
Later among the works it cites.
Characterization and learning of causal graphs with latent vaiables from soft interventions
M. Kocaoglu, A. Jaber, K. Shanmugam, and E. Bareinboim. 2019 · 2019
Later among the works it cites.
Machine learning in policy evaluation: new tools for causal inference
N. Kreif and K. DiazOrdaz. 2019 · 2019
Later among the works it cites.
Challenging common assumptions in the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, G. Ratsch, S. Gelly, B. Scholkopf, and Bachem O. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T.S. Richardson and J.M. Robins. 2013a · 2013
Cited alongside, same era.
Single World Intervention Graphs (SWIGS): a unification of the counterfactual and graphical approaches to causality
T.S. Richardson and J.M. Robins. 2013b · 2013
Cited alongside, same era.
Nonparametric sparsity and regularization
L. Rosasco, S. Villa, S. Mosci, M. Santoro, and A. Verri. 2013 · 2013
Cited alongside, same era.
l0-penalized maximum likelihood for sparse directed acyclic graphs
S. van de Geer and P. Bühlmann. 2013 · 2013
Cited alongside, same era.
CAM: Causal Additive Models, high-dimensional order search and penalized regression
P. Bühlmann, J. Peters, and J. et al. Ernest. 2014 · 2014
Cited alongside, same era.
Order-independent constraint-based causal structure learning
D. Colombo and M.H. Maathuis. 2014 · 2014
Cited alongside, same era.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. 2014 · 2014
Cited alongside, same era.
Later among the works it cites.
The neuro-symbolic concept learner: interpreting scenes, words, and sentences from natural supervision
J. Mao, C. Gan, P. Kohli, J. B. Tenenbaum, and J. Wu. 2019 · 2019
Later among the works it cites.
Improving sequential latent variable models with autoregressive flows
J. Marino, L. Chen, J. He, and S. Mandt. 2019 · 2019
Later among the works it cites.
Optimal training of fair predictive models
R. Nabi, D. Malinsky, and I. Shpitser. 2019 · 2019
Later among the works it cites.
Causal induction from visual observations for goal directed tasks
S. Nair, Y. Zhu, S. Savarese, and L. Fei-Fei. 2019 · 2019
Later among the works it cites.
A graph autoencoder approach to causal structure learning
I. Ng, S. Zhu, Z. Chen, and Z. Fang. 2019 · 2019
Later among the works it cites.
Inferring causation from time series in Earth system sciences
J. Runge, S. Bathiany, E. Bollt, G. Camps-Valls, D. Coumou, E. Deyle, C. Glymour, and M. et al. Kretschmer. 2019 · 2019
Later among the works it cites.
Causality for machine learning
B. Scholkopf. 2019 · 2019
Later among the works it cites.
How can we fool LIME and SHAP? Adversarial attacks on post hoc explanation methods
D. Slack, S. Hilgard, E. Jia, S. Singh, and H. Lakkaraju. 2019 · 2019
Later among the works it cites.
The blessings of multiple causes
Y. Wang and D. M. Blei. 2019 · 2019
Later among the works it cites.
pycausal
C.K. Wongchokprasitti, H. Hochheiser, J. Espino, E. Maguire, B. Andrews, M. Davis, and C. Inskip. 2019 · 2019
Later among the works it cites.
DAG-GNN: DAG structure learning with graph neural networks
Y. Yu, J. Chen, T. Gao, and M. Yu. 2019 · 2019
Later among the works it cites.
Learning causal state representations of partially observable environments
A. Zhang, Z.C. Lipton, L. Pineda, K. Azizzadenesheli, A. Anandkumar, L. Itti, J. Pineau, and T. Furlanello. 2019 · 2019
Later among the works it cites.
CausalWorld: a robotic manipulation benchmark for causal structure and transfer learning
O. Ahmed, F. Träuble, A. Goyal, A. Neitz, M. Wütrich, Y. Bengio, B. Schölkopf, and S. Bauer. 2020 · 2020
Later among the works it cites.
Causal Campbell-Goodhart’s law and reinforcement learning
H. Ashton. 2020 · 2020
Later among the works it cites.
On Pearl’s hierarchy and the foundations of causal inference
E. Bareinboim, J.D. Correa, D. Ibeling, and T. Icard. 2020 · 2020
Later among the works it cites.
D. Beaini, S. Passaro, V. Létourneau, W.L. Hamilton, G. Corso, and P. Liò. 2020 · 2020
Later among the works it cites.
Learning physical graph representations from visual scenes
M.B. Bear, C. Fan, D. Mrowca, Y. Li, S. Alter, A. Nayebi, J. Schwartz, L. Fei-Fei, J. Wu, J. B. Tenenbaum, and D.L.K. Yamis. 2020 · 2020
Later among the works it cites.
Differentiable causal discovery under unmeasured confounding
R. Bhattacharya, T. Nagarajan, D. Malinsky, and I. Shpitset. 2020 · 2020
Later among the works it cites.
Causal learning with sufficient statistics: an information bottleneck approach
D. Chicharro, M. Besserve, and S. Panzeri. 2020 · 2020
Later among the works it cites.
Bayesian causal structure learning with zero-inflated Poisson Bayesian networks
J. Choi, R. Chapkin, and Y. Ni. 2020 · 2020
Later among the works it cites.
A crash course in good and bad controls
C. Cinelli, A. Forney, and J. Pearl. 2020 · 2020
Later among the works it cites.
Disentangling causal effects for hierarchical reinforcement learning
O. Corcoll and R. Vicente. 2020 · 2020
Later among the works it cites.
D. Ding, F. Hill, A. Santoro, and M. Botvinick. 2020 · 2020
Later among the works it cites.
RELATE: Physically plausible multi-object scene synthesis using structured latent spaces
S. Ehrhardt, O. Groth, A. Monszpart, M. Engelcke, I. Posner, N. Mitra, and A. Vedaldi. 2020 · 2020
Later among the works it cites.
Learning directed graphical models from Gaussian data
K. Fitch. 2020 · 2020
Later among the works it cites.
A critical view of the structural causal model
T. Galanti, O. Nabati, and L. Wolf. 2020 · 2020
Later among the works it cites.
A.D. Garcez and L.C. Lamb. 2020 · 2020
Later among the works it cites.
Chacterizing distribution equivalence and structure learning for cyclic and acyclic directed graphs
AE. Ghassami, A. Yang, N. Kiyavash, and K. Zhang. 2020 · 2020
Later among the works it cites.
Bridging causality and learning: how do they benefit from each other?
M. Gong. 2020 · 2020
Later among the works it cites.
Inductive biases for deep learning of higher-level cognition
A. Goyal and Y. Bengio. 2020 · 2020
Later among the works it cites.
On the binding problem in artificial neural networks
K. Greff, S. van Steenkiste, and J. Schmidhuber. 2020 · 2020
Later among the works it cites.
The taboo against explicit causal inference in nonexperimental psychology
M.P. Grosz, J.M. Rohrer, and F. Thoemmes. 2020 · 2020
Later among the works it cites.
A survey of learning causality with data: Problems and methods
R. Guo, L. Cheng, J. Li, P.R. Hahn, and H. Liu. 2020 · 2020
Later among the works it cites.
Causal variables from reinforcement learning using generalized Bellman equations
T. Herlan. 2020 · 2020
Later among the works it cites.
Faster algorithms for Markov equivalence
Z. Hu and R. Evans. 2020 · 2020
Later among the works it cites.
Causal discovery from heterogeneous/nonstationary data with independent changes
B. Huang, K. Zhang, J. Zhang, J. Ramsey, Sanchez-Romero R., C. Glymour, and B. Schölkopf. 2020 · 2020
Later among the works it cites.
Causal discovery from soft interventions with unknown targets: characterization and learning
A. Jaber, M. Kocaoglu, K. Shanmugam, and E. Bareinboim. 2020 · 2020
Later among the works it cites.
Generative neurosymbolic machines
J. Jiang and S. Ahn. 2020 · 2020
Later among the works it cites.
Structural agnostic modeling: Adversarial learning of causal graphs
D. Kalainathan, O. Goudet, I. Guyon, D. Lopez-Paz, and M. Sebag. 2020 · 2020
Later among the works it cites.
Learning neural causal models from unknown interventions
N. R. Ke, O. Bilaniuk, A. Goyal, S. Bauer, H. Larochelle, B. Schölkopf, M. C. Mozer, C. Pal, and Y. Bengio. 2020a · 2020
Later among the works it cites.
Amortized learning of neural causal representations
N. R. Ke, J.X. Xang, J. Mitrovic, M. Szummer, and D. J. Rezende. 2020b · 2020
Later among the works it cites.
I. Khemakem, R.P. Monti, R. Leech, and A. Hyvärinen. 2020 · 2020
Later among the works it cites.
Normalizing flows: an introduction and review of current methods
I. Kobysev, S.J.D. Prince, and M.A. Brubaker. 2020 · 2020
Later among the works it cites.
CASTLE: regularization via auxiliary causal graph discovery
T. Kyono, Y. Zhang, and M. van der Schaar. 2020 · 2020
Later among the works it cites.
Gradient-based neural DAG learning
S. Lachapelle, P. Brouillard, T. Deleu, and S. Lacoste-Julien. 2020 · 2020
Later among the works it cites.
Scaling structural learning with NO-BEARS to infer causal transcriptome networks
H-C. Lee, M. Danieletto, R. Miotto, S.T. Cherng, and J.T. Dudley. 2020 · 2020
Later among the works it cites.
Accurate data-driven prediction does not mean high reproducibility
J. Li, L. Liu, T. D. Le, and J. Liu. 2020a · 2020
Later among the works it cites.
Causal discovery in physical systems from videos
Y. Li, A. Torralba, A. Anandkumar, D. Fox, and A. Garg. 2020b · 2020
Later among the works it cites.
A systematic review of causal methods enabling predictions under hypothetical interventions
L. Lin, M. Sperrin, D.A. Jenkins, G.P. Martin, and N. Peek. 2020 · 2020
Later among the works it cites.
Amortized causal discovery: Learning to infer causal graphs from time-series data
S. Lowe, D. Madras, R. Zemel, and M. Welling. 2020 · 2020
Later among the works it cites.
The Stanford Encyclopedia of Philosophy
P. Menzies and H. Beebee. 2020 · 2020
Later among the works it cites.
Causal adversarial network for learning conditional and interventional distributions
R. Moraffah, B. Moraffah, M. Karami, A. Raglin, and H. Liu. 2020 · 2020
Later among the works it cites.
Learning object-centric representations of multi-object scenes from multiple views
L. Nanbo, C. Eastwood, and R.B. Fisher. 2020 · 2020
Later among the works it cites.
E.C. Neto. 2020 · 2020
Later among the works it cites.
Masked gradient-based causal structure learning
I. Ng, Z. Fang, S. Zhu, Z. Chen, and J. Wang. 2020a · 2020
Later among the works it cites.
On the role of sparsity and DAG constraints for learning linear DAGs
I. Ng, AE. Ghassami, and K. Zhang. 2020b · 2020
Later among the works it cites.
DYNOTEARS: Structure learning from time-series data
R. Pamfil, N. Sriwattanaworachai, S. Desai, P. Pilgerstorfer, P. Beaumont, K. Georgatzis, and B. Aragam. 2020 · 2020
Later among the works it cites.
Causal models for dynamical systems
J. Peters, S. Bauer, and N. Pfister. 2020 · 2020
Later among the works it cites.
Causally correct partial models for reinforcement learning
D. J. Rezende, I. Danihelka, G. Papamakarios, N. R. Ke, R. Jiang, T. Weber, K. Gregor, H. Merzic, F. Viola, J. Wang, J. Mitrovic, F. Besse, I. Antonoglou, and L. Buesing. 2020 · 2020
Later among the works it cites.
Explaining the behavior of black-box prediction algorithms with causal learning
N. Sani, D. Malinsky, and I. Shpitser. 2020 · 2020
Later among the works it cites.
The hardness of conditional independence testing and the generalised covariance measure
R. D. Shah and J. Peters. 2020 · 2020
Later among the works it cites.
DoWhy: An end-to-end library for causal inference
A. Sharma and E. Kiciman. 2020 · 2020
Later among the works it cites.
Disentangled generative causal representation learning
X. Shen, F. Liu, H. Dong, Q. Lina, Z. Chen, and T. Zhang. 2020 · 2020
Later among the works it cites.
Double generative adversarial networks for conditional independence testing
C. Shi, T. Xu, and W. Bergsma. 2020 · 2020
Later among the works it cites.
S.A. Sontakke, A. Mehrjou, L. Itti, and B. Schölkopf. 2020 · 2020
Later among the works it cites.
Distinguishing cause from effect using quantiles: Bivariate quantile causal discovery
N. Tagasovska, V. Chavez-Demoulin, and T. Vatter. 2020 · 2020
Later among the works it cites.
dosearch: causal effect identification from multiple incomplete data sources
S. Tikka, A. Hyttinen, and J. Karvanen. 2020 · 2020
Later among the works it cites.
Learning DAGs without imposing acyclicity
G. Varando. 2020 · 2020
Later among the works it cites.
M. J. Vowels. 2020 · 2020
Later among the works it cites.
Targeted VAE: Structured inference and targeted learning for causal parameter estimation
M. J. Vowels, N.C. Camgoz, and R. Bowden. 2020a · 2020
Later among the works it cites.
NestedVAE: Isolating Common Factors via Weak Supervision
M. J. Vowels, N. C. Camgoz, and R. Bowden. 2020b · 2020
Later among the works it cites.
Causal discovery from incomplete data: a deep learning approach
Y. Wang, V. Menkovski, H. Wang, X. Du, and M. Pechenizkiy. 2020 · 2020
Later among the works it cites.
DAGs with No Fears: A closer look at continuous optimization for learning Bayesian networks
D. Wei, T. Gao, and Y. Yu. 2020 · 2020
Later among the works it cites.
S. Weichwald, M.E. Jakobsen, P.B. Mogensen, L. Petersen, N. Thams, and G. Varando. 2020 · 2020
Later among the works it cites.
Causality learning: a new perspective for interpretable machine learning
G. Xu, T.D. Duong, Q. Li, S. Liu, and X. Wang. 2020 · 2020
Later among the works it cites.
CausalVAE: disentangled representation learning via neural structural causal models
M. Yang, F. Liu, Z. Chen, X. Shen, J. Hao, and J. Wang. 2020 · 2020
Later among the works it cites.
L. Yao, Z. Chu, S. Li, Y. Li, J. Gao, and A. Zhang. 2020 · 2020
Later among the works it cites.
CLEVRER: collision events for video representation and reasoning
K. Yi, C. Gan, Y. Li, P. Kohli, J. Wu, A. Torralba, and J. B. Tenenbaum. 2020 · 2020
Later among the works it cites.
A simultaneous discover-identify approach to causal inference in linear models
C. Zhang, B. Chen, and J. Pearl. 2020a · 2020
Later among the works it cites.
Causal imitation learning with unobserved confounders
J. Zhang, D. Kumor, and E. Bareinboim. 2020b · 2020
Later among the works it cites.
Learning sparse nonparametric DAGs
X. Zheng, C. Dan, B. Aragam, P. Ravikumar, and E.P. Xing. 2020 · 2020
Later among the works it cites.
Efficient and scalable structure learning for Bayesian networks: Algorithms and Applications
R. Zhu, A. Pfadler, Z. Wu, Y. Han, X. Yang, F. Ye, Z. Qian, J. Zhou, and B. Cui. 2020b · 2020
Later among the works it cites.
Causal discovery with reinforcement learning
S. Zhu, I. Ng, and Z. Chen. 2020a · 2020
Later among the works it cites.
Systematic evaluation of causal discovery in visual model based reinforcement learning
anon. 2021 · 2021
Closest in time.
rEDM: Empirical Dynamic Modeling
J. Park, C. Smith, G. Sugihara, E. Deyle, E. Saberski, and H. Ye. 2021 · 2021
Closest in time.