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Nonparametric Identifiability of Causal Representations from Unknown
  Interventions
v1v2 (latest)

Nonparametric Identifiability of Causal Representations from Unknown Interventions

1 June 2023
Julius von Kügelgen
M. Besserve
Wendong Liang
Luigi Gresele
Armin Kekić
Elias Bareinboim
David M. Blei
Bernhard Schölkopf
    CML
ArXiv (abs)PDFHTML

Papers citing "Nonparametric Identifiability of Causal Representations from Unknown Interventions"

50 / 51 papers shown
Title
Identifiability of Deep Polynomial Neural Networks
Identifiability of Deep Polynomial Neural Networks
K. Usevich
Clara Dérand
Ricardo Augusto Borsoi
Marianne Clausel
24
0
0
20 Jun 2025
Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning
Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning
Jikai Jin
Vasilis Syrgkanis
Sham Kakade
Hanlin Zhang
ELM
125
1
0
12 Jun 2025
Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
Armin Kekić
Sergio Hernan Garrido Mejia
Bernhard Schölkopf
CML
126
0
0
05 Jun 2025
When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective
When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective
Beatrix M. G. Nielsen
Emanuele Marconato
Andrea Dittadi
Luigi Gresele
58
0
0
04 Jun 2025
From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual Matching
From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual Matching
Ruqi Bai
Yao Ji
Zeyu Zhou
David I. Inouye
OOD
29
0
0
30 May 2025
The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations
The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations
Dingling Yao
Shimeng Huang
Riccardo Cadei
Kun Zhang
Francesco Locatello
CML
144
0
0
23 May 2025
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research
Patrik Reizinger
Randall Balestriero
David Klindt
Wieland Brendel
199
1
0
17 Apr 2025
On the Value of Cross-Modal Misalignment in Multimodal Representation Learning
On the Value of Cross-Modal Misalignment in Multimodal Representation Learning
Yichao Cai
Yuhang Liu
Erdun Gao
Tianjiao Jiang
Zhen Zhang
Anton van den Hengel
Javen Qinfeng Shi
149
0
0
14 Apr 2025
Robustness of Nonlinear Representation Learning
Robustness of Nonlinear Representation Learning
Simon Buchholz
Bernhard Schölkopf
OOD
417
4
0
19 Mar 2025
On the Identifiability of Causal Abstractions
Xiusi Li
Sékou-Oumar Kaba
Siamak Ravanbakhsh
CML
113
0
0
13 Mar 2025
I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data?
Yuhang Liu
Dong Gong
Erdun Gao
Zhen Zhang
Zhen Zhang
Biwei Huang
Anton van den Hengel
Javen Qinfeng Shi
Javen Qinfeng Shi
461
1
0
12 Mar 2025
Sanity Checking Causal Representation Learning on a Simple Real-World System
Sanity Checking Causal Representation Learning on a Simple Real-World System
Juan L. Gamella
Simon Bing
Jakob Runge
CML
213
1
0
27 Feb 2025
Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens
Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens
Samuele Bortolotti
Emanuele Marconato
Paolo Morettin
Andrea Passerini
Stefano Teso
169
5
0
16 Feb 2025
What is causal about causal models and representations?
What is causal about causal models and representations?
Frederik Hytting Jørgensen
Luigi Gresele
S. Weichwald
CML
249
0
0
31 Jan 2025
Identifying General Mechanism Shifts in Linear Causal Representations
Identifying General Mechanism Shifts in Linear Causal Representations
Tianyu Chen
Kevin Bello
Francesco Locatello
Bryon Aragam
Pradeep Ravikumar
OODCML
100
3
0
31 Oct 2024
Identifiability Guarantees for Causal Disentanglement from Purely
  Observational Data
Identifiability Guarantees for Causal Disentanglement from Purely Observational Data
Ryan Welch
Jiaqi Zhang
Caroline Uhler
CMLOOD
96
1
0
31 Oct 2024
All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling
All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling
Emanuele Marconato
Sébastien Lachapelle
Sebastian Weichwald
Luigi Gresele
145
4
0
30 Oct 2024
Counterfactual Generative Modeling with Variational Causal Inference
Counterfactual Generative Modeling with Variational Causal Inference
Yulun Wu
Louie McConnell
Claudia Iriondo
CMLBDL
147
3
0
16 Oct 2024
Continual Learning of Nonlinear Independent Representations
Continual Learning of Nonlinear Independent Representations
Boyang Sun
Ignavier Ng
Guangyi Chen
Yifan Shen
Qirong Ho
Kun Zhang
OODCML
98
0
0
11 Aug 2024
Linear causal disentanglement via higher-order cumulants
Linear causal disentanglement via higher-order cumulants
Paula Leyes Carreno
Chiara Meroni
A. Seigal
CML
64
0
0
05 Jul 2024
Benchmarks for Reinforcement Learning with Biased Offline Data and
  Imperfect Simulators
Benchmarks for Reinforcement Learning with Biased Offline Data and Imperfect Simulators
Ori Linial
Guy Tennenholtz
Uri Shalit
OffRL
78
1
0
30 Jun 2024
Linear Causal Representation Learning from Unknown Multi-node
  Interventions
Linear Causal Representation Learning from Unknown Multi-node Interventions
Burak Varıcı
Emre Acartürk
Karthikeyan Shanmugam
A. Tajer
CML
75
1
0
09 Jun 2024
Marrying Causal Representation Learning with Dynamical Systems for Science
Marrying Causal Representation Learning with Dynamical Systems for Science
Dingling Yao
Caroline Muller
Francesco Locatello
CMLAI4CE
152
10
0
22 May 2024
Propensity Score Alignment of Unpaired Multimodal Data
Propensity Score Alignment of Unpaired Multimodal Data
Johnny Xi
Jason S. Hartford
66
5
0
02 Apr 2024
A Sparsity Principle for Partially Observable Causal Representation
  Learning
A Sparsity Principle for Partially Observable Causal Representation Learning
Danru Xu
Dingling Yao
Sébastien Lachapelle
Perouz Taslakian
Julius von Kügelgen
Francesco Locatello
Sara Magliacane
CML
95
14
0
13 Mar 2024
On the Challenges and Opportunities in Generative AI
On the Challenges and Opportunities in Generative AI
Laura Manduchi
Kushagra Pandey
Robert Bamler
Ryan Cotterell
Sina Daubener
...
F. Wenzel
Frank Wood
Stephan Mandt
Vincent Fortuin
Vincent Fortuin
288
22
0
28 Feb 2024
Implicit Causal Representation Learning via Switchable Mechanisms
Implicit Causal Representation Learning via Switchable Mechanisms
Shayan Shirahmad Gale Bagi
Zahra Gharaee
Oliver Schulte
Mark Crowley
CML
146
0
0
16 Feb 2024
Learning Interpretable Concepts: Unifying Causal Representation Learning
  and Foundation Models
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
Goutham Rajendran
Simon Buchholz
Bryon Aragam
Bernhard Schölkopf
Pradeep Ravikumar
AI4CE
175
23
0
14 Feb 2024
Counterfactual Image Editing
Counterfactual Image Editing
Yushu Pan
Elias Bareinboim
BDLCML
74
8
0
07 Feb 2024
Causal Representation Learning from Multiple Distributions: A General
  Setting
Causal Representation Learning from Multiple Distributions: A General Setting
Kun Zhang
Shaoan Xie
Ignavier Ng
Yujia Zheng
CMLOOD
124
23
0
07 Feb 2024
Toward the Identifiability of Comparative Deep Generative Models
Toward the Identifiability of Comparative Deep Generative Models
Romain Lopez
Jan-Christian Huetter
Ehsan Hajiramezanali
Jonathan Pritchard
Aviv Regev
80
2
0
29 Jan 2024
Targeted Reduction of Causal Models
Targeted Reduction of Causal Models
Armin Kekić
Bernhard Schölkopf
M. Besserve
CML
147
12
0
30 Nov 2023
An Interventional Perspective on Identifiability in Gaussian LTI Systems
  with Independent Component Analysis
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis
Goutham Rajendran
Patrik Reizinger
Wieland Brendel
Pradeep Ravikumar
CML
163
8
0
29 Nov 2023
Learning Causal Representations from General Environments:
  Identifiability and Intrinsic Ambiguity
Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity
Jikai Jin
Vasilis Syrgkanis
CML
100
6
0
21 Nov 2023
Multi-View Causal Representation Learning with Partial Observability
Multi-View Causal Representation Learning with Partial Observability
Dingling Yao
Danru Xu
Sébastien Lachapelle
Sara Magliacane
Perouz Taslakian
Georg Martius
Julius von Kügelgen
Francesco Locatello
CML
158
36
0
07 Nov 2023
Identifying Linearly-Mixed Causal Representations from Multi-Node
  Interventions
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
Simon Bing
Urmi Ninad
Jonas Wahl
Jakob Runge
CML
100
6
0
05 Nov 2023
Object-centric architectures enable efficient causal representation
  learning
Object-centric architectures enable efficient causal representation learning
Amin Mansouri
Jason S. Hartford
Yan Zhang
Yoshua Bengio
CMLOCLOOD
102
18
0
29 Oct 2023
Causal Representation Learning Made Identifiable by Grouping of
  Observational Variables
Causal Representation Learning Made Identifiable by Grouping of Observational Variables
H. Morioka
Aapo Hyvarinen
OODCMLBDL
124
10
0
24 Oct 2023
General Identifiability and Achievability for Causal Representation
  Learning
General Identifiability and Achievability for Causal Representation Learning
Burak Varici
Emre Acartürk
Karthikeyan Shanmugam
A. Tajer
CML
91
21
0
24 Oct 2023
From Identifiable Causal Representations to Controllable Counterfactual
  Generation: A Survey on Causal Generative Modeling
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling
Aneesh Komanduri
Xintao Wu
Yongkai Wu
Feng Chen
CMLOOD
133
11
0
17 Oct 2023
Deep Backtracking Counterfactuals for Causally Compliant Explanations
Deep Backtracking Counterfactuals for Causally Compliant Explanations
Klaus-Rudolf Kladny
Julius von Kügelgen
Bernhard Schölkopf
Michael Muehlebach
BDL
106
7
0
11 Oct 2023
Identifying Representations for Intervention Extrapolation
Identifying Representations for Intervention Extrapolation
Sorawit Saengkyongam
Ezgi Ozyilkan
Pradeep Ravikumar
Niklas Pfister
Jonas Peters
CMLOOD
84
16
0
06 Oct 2023
Multi-Domain Causal Representation Learning via Weak Distributional
  Invariances
Multi-Domain Causal Representation Learning via Weak Distributional Invariances
Kartik Ahuja
Amin Mansouri
Yixin Wang
CMLOOD
102
11
0
04 Oct 2023
Identifiability Guarantees for Causal Disentanglement from Soft
  Interventions
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Jiaqi Zhang
C. Squires
Kristjan Greenewald
Akash Srivastava
Karthikeyan Shanmugam
Caroline Uhler
CML
137
65
0
12 Jul 2023
Additive Decoders for Latent Variables Identification and
  Cartesian-Product Extrapolation
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
Sébastien Lachapelle
Divyat Mahajan
Ioannis Mitliagkas
Simon Lacoste-Julien
127
29
0
05 Jul 2023
Learning nonparametric latent causal graphs with unknown interventions
Learning nonparametric latent causal graphs with unknown interventions
Yibo Jiang
Bryon Aragam
CML
101
24
0
05 Jun 2023
Learning Linear Causal Representations from Interventions under General
  Nonlinear Mixing
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
Simon Buchholz
Goutham Rajendran
Elan Rosenfeld
Bryon Aragam
Bernhard Schölkopf
Pradeep Ravikumar
CML
110
65
0
04 Jun 2023
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and
  Mitigation of Reasoning Shortcuts
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
Emanuele Marconato
Stefano Teso
Antonio Vergari
Andrea Passerini
113
36
0
31 May 2023
Causal Component Analysis
Causal Component Analysis
Wendong Liang
Armin Kekić
Julius von Kügelgen
Simon Buchholz
M. Besserve
Luigi Gresele
Bernhard Schölkopf
CML
162
38
0
26 May 2023
Emerging Synergies in Causality and Deep Generative Models: A Survey
Emerging Synergies in Causality and Deep Generative Models: A Survey
Guanglin Zhou
Shaoan Xie
Guang-Yuan Hao
Shiming Chen
Erdun Gao
Xiwei Xu
Chen Wang
Liming Zhu
Lina Yao
Kun Zhang
AI4CE
147
11
0
29 Jan 2023
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