Learning high-dimensional directed acyclic graphs with latent and selection variables

29 April 2011

Papers citing "Learning high-dimensional directed acyclic graphs with latent and selection variables"

50 / 62 papers shown

Title
dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Data Adèle Ribeiro Dominik Heider CML 26 0 0 10 May 2025
Characterization and Learning of Causal Graphs from Hard Interventions Zihan Zhou Muhammad Qasim Elahi Murat Kocaoglu CML 87 0 0 02 May 2025
Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasets Christine W Bang Vanessa Didelez CML 59 0 0 27 Mar 2025
SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph Mátyás Schubert Tom Claassen Sara Magliacane CML 71 0 0 11 Feb 2025
Causal Discovery via Bayesian Optimization Bao Duong Sunil Gupta Thin Nguyen 44 0 0 28 Jan 2025
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning Numair Sani Daniel Malinsky I. Shpitser CML 76 15 0 10 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 Mingming Gong Yi-An Ma Biwei Huang 39 1 0 08 Oct 2024
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning Ashka Shah Adela DePavia Nathaniel Hudson Ian T. Foster Rick L. Stevens CML 31 1 0 10 Jun 2024
Local Causal Structure Learning in the Presence of Latent Variables Feng Xie Zheng Li Peng Wu Yan Zeng Chunchen Liu Zhi Geng CML 34 2 0 25 May 2024
Continual Model-based Reinforcement Learning for Data Efficient Wireless Network Optimisation Cengis Hasan Alexandros Agapitos David Lynch Alberto Castagna Giorgio Cruciata Hao Wang Aleksandar Milenovic 43 0 0 30 Apr 2024
Towards Automated Causal Discovery: a case study on 5G telecommunication data Konstantina Biza Antonios Ntroumpogiannis Sofia Triantafillou Ioannis Tsamardinos 31 0 0 22 Feb 2024
CURE: Simulation-Augmented Auto-Tuning in Robotics Md. Abir Hossen Sonam Kharade Jason M. O'Kane B. Schmerl David Garlan Pooyan Jamshidi 24 1 0 08 Feb 2024
On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factors Xinshuai Dong Haoyue Dai Yewen Fan Songyao Jin Sathyamoorthy Rajendran Kun Zhang CML 39 1 0 28 Dec 2023
Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants Wei Chen Zhiyi Huang Ruichu Cai Zhifeng Hao Kun Zhang CML 17 3 0 19 Dec 2023
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
Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets Tiago da Silva Eliezer de Souza da Silva Adèle Ribeiro António Góis Dominik Heider Samuel Kaski Diego Mesquita CML 49 6 0 21 Sep 2023
Neuro-Causal Factor Analysis Alex Markham Ming-Yu Liu Bryon Aragam Liam Solus CML 26 3 0 31 May 2023
Causal Discovery with Latent Confounders Based on Higher-Order Cumulants Ruichu Cai Zhiyi Huang Wei-Neng Chen Z. Hao Kun Zhang CML 32 9 0 31 May 2023
Causality-Aided Trade-off Analysis for Machine Learning Fairness Zhenlan Ji Pingchuan Ma Shuai Wang Yanhui Li FaML 31 7 0 22 May 2023
A Survey on Causal Discovery: Theory and Practice Alessio Zanga Fabio Stella CML 27 38 0 17 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
CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data Muhammad Hasan Ferdous Uzma Hasan Md. Osman Gani CML 30 3 0 07 Feb 2023
A Survey of Methods, Challenges and Perspectives in Causality Gael Gendron Michael Witbrock Gillian Dobbie OOD AI4CE CML 29 12 0 01 Feb 2023
Data-Driven Estimation of Heterogeneous Treatment Effects Christopher Tran Keith Burghardt Kristina Lerman Elena Zheleva CML 27 1 0 16 Jan 2023
Deep Learning of Causal Structures in High Dimensions Kai Lagemann C. Lagemann B. Taschler S. Mukherjee CML BDL AI4CE 30 29 0 09 Dec 2022
Fast Parallel Bayesian Network Structure Learning Jiantong Jiang Zeyi Wen Ajmal Saeed Mian 25 6 0 08 Dec 2022
Initial Results for Pairwise Causal Discovery Using Quantitative Information Flow Felipe Giori Flavio Figueiredo CML 14 1 0 02 Dec 2022
Deep Causal Learning: Representation, Discovery and Inference Zizhen Deng Xiaolong Zheng Hu Tian D. Zeng CML BDL 31 11 0 07 Nov 2022
Causal Inference for De-biasing Motion Estimation from Robotic Observational Data Junhong Xu Kai-Li Yin J. Gregory Lantao Liu CML 13 3 0 17 Oct 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 32 25 0 30 Aug 2022
Valid Inference after Causal Discovery Paula Gradu Tijana Zrnic Yixin Wang Michael I. Jordan CML 26 8 0 11 Aug 2022
Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data Debo Cheng Jiuyong Li Lin Liu Kui Yu Thuc Duy Lee Hefei University of Technology CML 19 13 0 04 Jun 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
Unicorn: Reasoning about Configurable System Performance through the lens of Causality Md Shahriar Iqbal R. Krishna Mohammad Ali Javidian Baishakhi Ray Pooyan Jamshidi LRM 26 27 0 20 Jan 2022
Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias R. Y. Rohekar Shami Nisimov Yaniv Gurwicz Gal Novik CML 135 25 0 07 Nov 2021
Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias S. Akbari Ehsan Mokhtarian AmirEmad Ghassami Negar Kiyavash CML 11 24 0 22 Oct 2021
A survey of Bayesian Network structure learning N. K. Kitson Anthony C. Constantinou Zhi-gao Guo Yang Liu Kiattikun Chobtham CML 24 182 0 23 Sep 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
Differentiable Causal Discovery Under Unmeasured Confounding Rohit Bhattacharya Tushar Nagarajan Daniel Malinsky I. Shpitser CML 15 60 0 14 Oct 2020
Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data Anthony C. Constantinou Yang Liu Kiattikun Chobtham Zhi-gao Guo N. K. Kitson CML 30 61 0 18 May 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 22 187 0 02 Feb 2020
Causality-based Feature Selection: Methods and Evaluations Kui Yu Xianjie Guo Lin Liu Jiuyong Li Hao Wang Zhaolong Ling Xindong Wu CML 24 92 0 17 Nov 2019
Ordering-Based Causal Structure Learning in the Presence of Latent Variables D. Bernstein Basil Saeed C. Squires Caroline Uhler CML 27 40 0 20 Oct 2019
The Global Markov Property for a Mixture of DAGs Eric V. Strobl 14 3 0 12 Sep 2019
Causal Discovery with a Mixture of DAGs Eric V. Strobl CML 16 17 0 28 Jan 2019
A Survey of Learning Causality with Data: Problems and Methods Ruocheng Guo Lu Cheng Jundong Li P. R. Hahn Huan Liu CML 32 168 0 25 Sep 2018
Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks Alex E. White Matthieu Vignes CML 15 5 0 04 May 2018
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
Learning Large-Scale Bayesian Networks with the sparsebn Package Bryon Aragam J. Gu Qing Zhou CML 19 55 0 11 Mar 2017