CAM: Causal additive models, high-dimensional order search and penalized regression

6 October 2013

Papers citing "CAM: Causal additive models, high-dimensional order search and penalized regression"

50 / 54 papers shown

Title
Identifying Causal Direction via Variational Bayesian Compression Quang-Duy Tran Bao Duong Phuoc Nguyen Thin Nguyen CML 34 0 0 12 May 2025
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions Juan L. Gamella Armeen Taeb C. Heinze-Deml Peter Buhlmann CML 94 8 0 13 Mar 2025
Causal Discovery via Bayesian Optimization Bao Duong Sunil Gupta Thin Nguyen 44 0 0 28 Jan 2025
Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis Hiroshi Yokoyama Ryusei Shingaki Kaneharu Nishino Shohei Shimizu Thong Pham CML 75 0 0 11 Nov 2024
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery Yingyu Lin Yuxing Huang Wenqin Liu Haoran Deng Ignavier Ng Kun Zhang Mingming Gong Yi-An Ma Biwei Huang 39 1 0 08 Oct 2024
Score matching through the roof: linear, nonlinear, and latent variables causal discovery Francesco Montagna P. M. Faller Patrick Bloebaum Elke Kirschbaum Francesco Locatello CML 87 0 0 26 Jul 2024
Standardizing Structural Causal Models Weronika Ormaniec Scott Sussex Lars Lorch Bernhard Schölkopf Andreas Krause CML 56 5 0 17 Jun 2024
Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency Alan Nawzad Amin Andrew Gordon Wilson CML 34 1 0 13 Jun 2024
Demystifying amortized causal discovery with transformers Francesco Montagna Max Cairney-Leeming Dhanya Sridhar Francesco Locatello CML 57 1 0 27 May 2024
Learning Directed Acyclic Graphs from Partial Orderings Ali Shojaie Wenyu Chen CML 45 0 0 24 Mar 2024
Sample, estimate, aggregate: A recipe for causal discovery foundation models Menghua Wu Yujia Bao Regina Barzilay Tommi Jaakkola CML 49 7 0 02 Feb 2024
Recovering Linear Causal Models with Latent Variables via Cholesky Factorization of Covariance Matrix Yunfeng Cai Xu Li Ming Sun Ping Li CML 21 1 0 01 Nov 2023
Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice Damian Machlanski Spyridon Samothrakis Paul Clarke CML 36 1 0 27 Oct 2023
Causal Representation Learning Made Identifiable by Grouping of Observational Variables H. Morioka Aapo Hyvarinen OOD CML BDL 33 9 0 24 Oct 2023
Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery Victor-Alexandru Darvariu Stephen Hailes Mirco Musolesi CML 41 2 0 20 Oct 2023
Differentiable Bayesian Structure Learning with Acyclicity Assurance Quang-Duy Tran Phuoc Nguyen Bao Duong Thin Nguyen 32 2 0 04 Sep 2023
Order-based Structure Learning with Normalizing Flows Hamidreza Kamkari Vahid Balazadeh Meresht Vahid Zehtab Rahul G. Krishnan CML 31 1 0 14 Aug 2023
Learning nonparametric DAGs with incremental information via high-order HSIC Yafei Wang Jianguo Liu CML 27 0 0 11 Aug 2023
$$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery$ $\texttt{causalAssembly}$ : Generating Realistic Production Data for Benchmarking Causal Discovery Konstantin Göbler Tobias Windisch Mathias Drton T. Pychynski Steffen Sonntag Martin Roth CML 73 10 0 19 Jun 2023
Learning DAGs from Data with Few Root Causes Panagiotis Misiakos Chris Wendler Markus Püschel CML 40 10 0 25 May 2023
A Survey on Causal Discovery: Theory and Practice Alessio Zanga Fabio Stella CML 27 38 0 17 May 2023
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models Alexander G. Reisach Myriam Tami C. Seiler Antoine Chambaz S. Weichwald CML 36 19 0 31 Mar 2023
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data Uzma Hasan Emam Hossain Md. Osman Gani CML AI4TS 31 24 0 27 Mar 2023
Rank-Based Causal Discovery for Post-Nonlinear Models Grigor Keropyan David Strieder Mathias Drton CML 8 6 0 23 Feb 2023
On Learning Necessary and Sufficient Causal Graphs Hengrui Cai Yixin Wang Michael Jordan Rui Song CML 24 12 0 29 Jan 2023
DAG Learning on the Permutahedron Valentina Zantedeschi Luca Franceschi Jean Kaddour Matt J. Kusner Vlad Niculae 27 11 0 27 Jan 2023
Variation-based Cause Effect Identification Mohamed Amine ben Salem Karim Barsim Bin Yang CML 24 0 0 22 Nov 2022
Confounder Balancing for Instrumental Variable Regression with Latent Variable Anpeng Wu Kun Kuang Ruoxuan Xiong Bo Li Fei Wu CML 38 0 0 18 Nov 2022
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models Maximilian Kertel Stefan Harmeling Markus Pauly CML 30 4 0 26 Oct 2022
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization Kevin Bello Bryon Aragam Pradeep Ravikumar 61 78 0 16 Sep 2022
Do learned representations respect causal relationships? Lan Wang Vishnu Naresh Boddeti NAI CML OOD 29 6 0 02 Apr 2022
Score matching enables causal discovery of nonlinear additive noise models Paul Rolland V. Cevher Matthäus Kleindessner Chris Russel Bernhard Schölkopf Dominik Janzing Francesco Locatello CML 54 84 0 08 Mar 2022
Differentiable Causal Discovery Under Latent Interventions Gonccalo R. A. Faria André F. T. Martins Mário A. T. Figueiredo BDL CML OOD 45 23 0 04 Mar 2022
Optimal estimation of Gaussian DAG models Ming Gao W. Tai Bryon Aragam 38 9 0 25 Jan 2022
Distributed Learning of Generalized Linear Causal Networks Qiaoling Ye Arash A. Amini Qing Zhou CML OOD AI4CE 38 16 0 23 Jan 2022
gCastle: A Python Toolbox for Causal Discovery Keli Zhang Shengyu Zhu Marcus Kalander Ignavier Ng Junjian Ye Zhitang Chen Lujia Pan CML 27 60 0 30 Nov 2021
A Fast Non-parametric Approach for Local Causal Structure Learning Mona Azadkia Armeen Taeb Peter Buhlmann CML 27 3 0 29 Nov 2021
Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers Wei Zhou Xin He Wei Zhong Junhui Wang CML 33 3 0 01 Nov 2021
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families Goutham Rajendran Bohdan Kivva Ming Gao Bryon Aragam 27 17 0 10 Oct 2021
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures Yashas Annadani Jonas Rothfuss Alexandre Lacoste Nino Scherrer Anirudh Goyal Yoshua Bengio Stefan Bauer BDL CML 32 48 0 14 Jun 2021
DAGs with No Curl: An Efficient DAG Structure Learning Approach Yue Yu Tian Gao Naiyu Yin Q. Ji CML 22 59 0 14 Jun 2021
Ordering-Based Causal Discovery with Reinforcement Learning Xiaoqiang Wang Yali Du Shengyu Zhu Liangjun Ke Zhitang Chen Jianye Hao Jun Wang CML 26 63 0 14 May 2021
Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data A. Lawrence Marcus Kaiser Rui Sampaio Maksim Sipos CML AI4TS 19 17 0 16 Apr 2021
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery M. Vowels Necati Cihan Camgöz Richard Bowden CML 37 296 0 03 Mar 2021
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game Alexander G. Reisach C. Seiler S. Weichwald CML 21 136 0 26 Feb 2021
Learning Causal Semantic Representation for Out-of-Distribution Prediction Chang-Shu Liu Xinwei Sun Jindong Wang Haoyue Tang Tao Li Tao Qin Wei Chen Tie-Yan Liu CML OODD OOD 32 104 0 03 Nov 2020
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks Dennis L. Wei Tian Gao Yue Yu CML 56 71 0 18 Oct 2020
Conditionally-additive-noise Models for Structure Learning D. Chicharro S. Panzeri I. Shpitser CML 11 4 0 20 May 2019
Causal Discovery Toolbox: Uncover causal relationships in Python Diviyan Kalainathan Olivier Goudet CML 17 82 0 06 Mar 2019
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs Diviyan Kalainathan Olivier Goudet Isabelle M Guyon David Lopez-Paz Michèle Sebag CML 24 93 0 13 Mar 2018