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Causal Discovery with General Non-Linear Relationships Using Non-Linear
  ICA

Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA

19 April 2019
R. Monti
Kun Zhang
Aapo Hyvarinen
    CML
ArXivPDFHTML

Papers citing "Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA"

11 / 11 papers shown
Title
Demystifying amortized causal discovery with transformers
Demystifying amortized causal discovery with transformers
Francesco Montagna
Max Cairney-Leeming
Dhanya Sridhar
Francesco Locatello
CML
101
1
0
27 May 2024
Sample, estimate, aggregate: A recipe for causal discovery foundation models
Sample, estimate, aggregate: A recipe for causal discovery foundation models
Menghua Wu
Yujia Bao
Regina Barzilay
Tommi Jaakkola
CML
89
7
0
02 Feb 2024
Causal Discovery from Conditionally Stationary Time Series
Causal Discovery from Conditionally Stationary Time Series
Carles Balsells-Rodas
Ruibo Tu
Tanmayee Narendra
Yingzhen Li
Gabriele Schweikert
Hedvig Kjellström
Yingzhen Li
AI4TS
BDL
CML
126
6
0
12 Oct 2021
Counterfactual Fairness
Counterfactual Fairness
Matt J. Kusner
Joshua R. Loftus
Chris Russell
Ricardo M. A. Silva
FaML
215
1,577
0
20 Mar 2017
Unsupervised Feature Extraction by Time-Contrastive Learning and
  Nonlinear ICA
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Aapo Hyvarinen
H. Morioka
CML
OOD
AI4TS
61
409
0
20 May 2016
Causal inference using invariant prediction: identification and
  confidence intervals
Causal inference using invariant prediction: identification and confidence intervals
J. Peters
Peter Buhlmann
N. Meinshausen
OOD
120
969
0
06 Jan 2015
Causal Discovery with Continuous Additive Noise Models
Causal Discovery with Continuous Additive Noise Models
Jonas Peters
Joris Mooij
Dominik Janzing
Bernhard Schölkopf
CML
103
569
0
26 Sep 2013
On the Number of Experiments Sufficient and in the Worst Case Necessary
  to Identify All Causal Relations Among N Variables
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables
F. Eberhardt
Clark Glymour
R. Scheines
92
154
0
04 Jul 2012
On Causal and Anticausal Learning
On Causal and Anticausal Learning
Bernhard Schölkopf
Dominik Janzing
J. Peters
Eleni Sgouritsa
Kun Zhang
Joris Mooij
CML
81
607
0
27 Jun 2012
On the Identifiability of the Post-Nonlinear Causal Model
On the Identifiability of the Post-Nonlinear Causal Model
Kun Zhang
Aapo Hyvarinen
CML
194
564
0
09 May 2012
DirectLiNGAM: A direct method for learning a linear non-Gaussian
  structural equation model
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model
Shohei Shimizu
Takanori Inazumi
Yasuhiro Sogawa
Aapo Hyvarinen
Yoshinobu Kawahara
Takashi Washio
P. Hoyer
K. Bollen
CML
102
510
0
13 Jan 2011
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