Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game

26 February 2021

Papers citing "Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game"

45 / 45 papers shown

Title
CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models Benjamin Herdeanu Juan Nathaniel Carla Roesch Jatan Buch Gregor Ramien Johannes Haux Pierre Gentine CML AI4CE 75 0 0 22 May 2025
Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery Rebecca Herman Jonas Wahl Urmi Ninad Jakob Runge 79 1 0 21 Mar 2025
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions Juan L. Gamella Armeen Taeb C. Heinze-Deml Peter Buhlmann CML 119 8 0 13 Mar 2025
Addressing pitfalls in implicit unobserved confounding synthesis using explicit block hierarchical ancestral sampling Xudong Sun Alex Markham Pratik Misra Carsten Marr CML 123 0 0 12 Mar 2025
Your Assumed DAG is Wrong and Here's How To Deal With It Kirtan Padh Zhufeng Li Cecilia Casolo Niki Kilbertus CML 78 0 0 24 Feb 2025
Causal Discovery via Bayesian Optimization Bao Duong Sunil Gupta Thin Nguyen 108 0 0 28 Jan 2025
A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery Yingyu Lin Yuxing Huang Wenqin Liu Haoran Deng Ignavier Ng Kun Zhang Biwei Huang Yi-An Ma Zhen Zhang 68 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 113 0 0 26 Jul 2024
Standardizing Structural Causal Models Weronika Ormaniec Scott Sussex Lars Lorch Bernhard Schölkopf Andreas Krause CML 77 6 0 17 Jun 2024
Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm Mathieu Chevalley Patrick Schwab Arash Mehrjou 93 1 0 28 May 2024
Demystifying amortized causal discovery with transformers Francesco Montagna Max Cairney-Leeming Dhanya Sridhar Francesco Locatello CML 96 1 0 27 May 2024
ALCM: Autonomous LLM-Augmented Causal Discovery Framework Elahe Khatibi Mahyar Abbasian Zhongqi Yang Iman Azimi Amir M. Rahmani 88 14 0 02 May 2024
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes Georg Manten Cecilia Casolo E. Ferrucci Søren Wengel Mogensen C. Salvi Niki Kilbertus CML BDL 158 12 0 28 Feb 2024
Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders Christian Toth Christian Knoll Franz Pernkopf Robert Peharz CML 99 1 0 22 Feb 2024
Sample, estimate, aggregate: A recipe for causal discovery foundation models Menghua Wu Yujia Bao Regina Barzilay Tommi Jaakkola CML 81 7 0 02 Feb 2024
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach Masayuki Takayama Tadahisa Okuda Thong Pham T. Ikenoue Shingo Fukuma Shohei Shimizu Akiyoshi Sannai 107 20 0 02 Feb 2024
Unsuitability of NOTEARS for Causal Graph Discovery Marcus Kaiser Maksim Sipos CML 79 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 89 300 0 03 Mar 2021
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks Dennis L. Wei Tian Gao Yue Yu CML 72 71 0 18 Oct 2020
Differentiable Causal Discovery Under Unmeasured Confounding Rohit Bhattacharya Tushar Nagarajan Daniel Malinsky I. Shpitser CML 58 60 0 14 Oct 2020
Differentiable Causal Discovery from Interventional Data P. Brouillard Sébastien Lachapelle Alexandre Lacoste Simon Lacoste-Julien Alexandre Drouin CML 56 186 0 03 Jul 2020
A polynomial-time algorithm for learning nonparametric causal graphs Ming Gao Yi Ding Bryon Aragam CML 21 32 0 22 Jun 2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs Ignavier Ng AmirEmad Ghassami Kun Zhang CML 50 190 0 17 Jun 2020
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values S. Weichwald M. E. Jakobsen Phillip B. Mogensen Lasse Petersen Nikolaj Thams Gherardo Varando CML AI4TS 71 27 0 21 Feb 2020
DYNOTEARS: Structure Learning from Time-Series Data Roxana Pamfil Nisara Sriwattanaworachai Shaan Desai Philip Pilgerstorfer Paul Beaumont K. Georgatzis Bryon Aragam CML AI4TS BDL 64 190 0 02 Feb 2020
Causality for Machine Learning Bernhard Schölkopf CML AI4CE LRM 82 461 0 24 Nov 2019
Scaling structural learning with NO-BEARS to infer causal transcriptome networks Hao-Chih Lee M. Danieletto Riccardo Miotto S. Cherng J. Dudley CML 35 46 0 31 Oct 2019
Learning Sparse Nonparametric DAGs Xun Zheng Chen Dan Bryon Aragam Pradeep Ravikumar Eric Xing CML 150 260 0 29 Sep 2019
Gradient-Based Neural DAG Learning Sébastien Lachapelle P. Brouillard T. Deleu Simon Lacoste-Julien BDL CML 50 273 0 05 Jun 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks Yue Yu Jie Chen Tian Gao Mo Yu BDL CML GNN 67 485 0 22 Apr 2019
On Causal Discovery with Equal Variance Assumption Wenyu Chen Mathias Drton Y Samuel Wang CML 60 85 0 09 Jul 2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng Bryon Aragam Pradeep Ravikumar Eric Xing NoLa CML OffRL 79 937 0 04 Mar 2018
Learning linear structural equation models in polynomial time and sample complexity Asish Ghoshal Jean Honorio CML 72 84 0 15 Jul 2017
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity Asish Ghoshal Jean Honorio CML TPM 65 55 0 03 Mar 2017
Concave Penalized Estimation of Sparse Gaussian Bayesian Networks Bryon Aragam Qing Zhou CML 97 107 0 04 Jan 2014
High-dimensional learning of linear causal networks via inverse covariance estimation Po-Ling Loh Peter Buhlmann CML 95 189 0 14 Nov 2013
CAM: Causal additive models, high-dimensional order search and penalized regression Peter Buhlmann J. Peters J. Ernest CML 104 323 0 06 Oct 2013
Strong Completeness and Faithfulness in Bayesian Networks Christopher Meek 98 308 0 20 Feb 2013
A Transformational Characterization of Equivalent Bayesian Network Structures D. M. Chickering 226 417 0 20 Feb 2013
Learning Equivalence Classes of Bayesian Networks Structures D. M. Chickering 94 832 0 13 Feb 2013
Large-Sample Learning of Bayesian Networks is NP-Hard D. M. Chickering Christopher Meek David Heckerman BDL 109 793 0 19 Oct 2012
Identifiability of Gaussian structural equation models with equal error variances J. Peters Peter Buhlmann CML 167 336 0 11 May 2012
On the Identifiability of the Post-Nonlinear Causal Model Kun Zhang Aapo Hyvarinen CML 169 564 0 09 May 2012
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 97 510 0 13 Jan 2011
Penalized Likelihood Methods for Estimation of Sparse High Dimensional Directed Acyclic Graphs Ali Shojaie George Michailidis CML 125 200 0 28 Nov 2009