On the Role of Sparsity and DAG Constraints for Learning Linear DAGs

17 June 2020

Papers citing "On the Role of Sparsity and DAG Constraints for Learning Linear DAGs"

37 / 37 papers shown

Title
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions Juan L. Gamella Armeen Taeb C. Heinze-Deml Peter Buhlmann CML 91 7 0 13 Mar 2025
Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning Albert Wilcox Mohamed Ghanem Masoud Moghani Pierre Barroso Benjamin Joffe Animesh Garg 50 0 0 06 Mar 2025
Causal Discovery via Bayesian Optimization Bao Duong Sunil Gupta Thin Nguyen 44 0 0 28 Jan 2025
LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Data Yue Cheng Jiajun Zhang Weiwei Xing Xiaoyu Guo Yue Cheng Witold Pedrycz CML 32 0 0 25 Oct 2024
Standardizing Structural Causal Models Weronika Ormaniec Scott Sussex Lars Lorch Bernhard Schölkopf Andreas Krause CML 50 5 0 17 Jun 2024
Demystifying amortized causal discovery with transformers Francesco Montagna Max Cairney-Leeming Dhanya Sridhar Francesco Locatello CML 57 1 0 27 May 2024
ProDAG: Projection-Induced Variational Inference for Directed Acyclic Graphs Ryan Thompson Edwin V. Bonilla Robert Kohn 37 0 0 24 May 2024
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders Christian Toth Christian Knoll Franz Pernkopf Robert Peharz CML 43 1 0 22 Feb 2024
Causal Representation Learning Made Identifiable by Grouping of Observational Variables H. Morioka Aapo Hyvarinen OOD CML BDL 25 9 0 24 Oct 2023
Identifiability of Homoscedastic Linear Structural Equation Models using Algebraic Matroids Mathias Drton Benjamin Hollering June Wu 14 1 0 03 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 70 9 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
Discovering Causal Relations and Equations from Data Gustau Camps-Valls Andreas Gerhardus Urmi Ninad Gherardo Varando Georg Martius E. Balaguer-Ballester Ricardo Vinuesa Emiliano Díaz L. Zanna Jakob Runge PINN AI4Cl AI4CE CML 35 72 0 21 May 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
Brain Effective Connectome based on fMRI and DTI Data: Bayesian Causal Learning and Assessment Abdolmahdi Bagheri Mahdi Dehshiri Yamin Bagheri Alireza Akhondi-Asl Babak Nadjar Araabi 11 4 0 10 Feb 2023
Directed Acyclic Graphs With Tears Zhichao Chen Zhiqiang Ge CML 28 5 0 04 Feb 2023
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization Dongjie Wang Zhengzhang Chen Jingchao Ni Liang Tong Zheng Wang Yanjie Fu Haifeng Chen AI4CE 11 17 0 03 Feb 2023
DAG Learning on the Permutahedron Valentina Zantedeschi Luca Franceschi Jean Kaddour Matt J. Kusner Vlad Niculae 25 11 0 27 Jan 2023
Causal Structural Learning from Time Series: A Convex Optimization Approach S. Wei Yao Xie CML 27 2 0 26 Jan 2023
Towards Privacy-Aware Causal Structure Learning in Federated Setting Jianli Huang Xianjie Guo Kui Yu Fuyuan Cao Jiye Liang FedML 29 9 0 13 Nov 2022
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks Yue Yu Xuan Kan Hejie Cui Ran Xu Yu Zheng ... Kun Zhang Razieh Nabi Ying Guo Chaogang Zhang Carl Yang 11 17 0 01 Nov 2022
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization Kevin Bello Bryon Aragam Pradeep Ravikumar 59 78 0 16 Sep 2022
Truncated Matrix Power Iteration for Differentiable DAG Learning Zhen Zhang Ignavier Ng Dong Gong Yuhang Liu Ehsan Abbasnejad Mingming Gong Kun Zhang Javen Qinfeng Shi 22 25 0 30 Aug 2022
CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods Giovanni Menegozzo Diego DallÁlba Paolo Fiorini 17 7 0 02 Aug 2022
Large-Scale Differentiable Causal Discovery of Factor Graphs Romain Lopez Jan-Christian Hütter J. Pritchard Aviv Regev CML AI4CE 45 40 0 15 Jun 2022
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery Chris Cundy Aditya Grover Stefano Ermon CML 40 72 0 06 Dec 2021
gCastle: A Python Toolbox for Causal Discovery Keli Zhang Shengyu Zhu Marcus Kalander Ignavier Ng Junjian Ye Zhitang Chen Lujia Pan CML 24 60 0 30 Nov 2021
Towards Federated Bayesian Network Structure Learning with Continuous Optimization Ignavier Ng Kun Zhang FedML 42 38 0 18 Oct 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
Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data A. Lawrence Marcus Kaiser Rui Sampaio Maksim Sipos CML AI4TS 16 17 0 16 Apr 2021
Unsuitability of NOTEARS for Causal Graph Discovery Marcus Kaiser Maksim Sipos CML 27 65 0 12 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 15 136 0 26 Feb 2021
Masked Gradient-Based Causal Structure Learning Ignavier Ng Shengyu Zhu Zhuangyan Fang Haoyang Li Zhitang Chen Jun Wang CML 80 117 0 18 Oct 2019
Learning Sparse Nonparametric DAGs Xun Zheng Chen Dan Bryon Aragam Pradeep Ravikumar Eric P. Xing CML 109 258 0 29 Sep 2019
Causal Inference in the Presence of Latent Variables and Selection Bias Peter Spirtes Christopher Meek Thomas S. Richardson CML 147 435 0 20 Feb 2013
A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed Cyclic Graphical Models Thomas S. Richardson 65 45 0 13 Feb 2013