Causal Inference in the Presence of Latent Variables and Selection Bias

20 February 2013

Papers citing "Causal Inference in the Presence of Latent Variables and Selection Bias"

40 / 140 papers shown

Title
Leveraging directed causal discovery to detect latent common causes Ciarán M. Gilligan-Lee Chris Hart Jonathan G. Richens Saurabh Johri CML 13 16 0 22 Oct 2019
Measurement Dependence Inducing Latent Causal Models Alex Markham Moritz Grosse-Wentrup CML 11 15 0 19 Oct 2019
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables Saber Salehkaleybar AmirEmad Ghassami Negar Kiyavash Kun Zhang CML 6 41 0 11 Aug 2019
Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation Ruibo Tu Kun Zhang Bo Christer Bertilson Hedvig Kjellström Cheng Zhang OOD CML 11 38 0 04 Jun 2019
Intervention in undirected Ising graphs and the partition function L. Waldorp M. Marsman CML 12 2 0 27 May 2019
Online Causal Structure Learning in the Presence of Latent Variables D. Kocacoban James Cussens CML 6 4 0 30 Apr 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks Yue Yu Jie Chen Tian Gao Mo Yu BDL CML GNN 8 476 0 22 Apr 2019
A Survey of Learning Causality with Data: Problems and Methods Ruocheng Guo Lu Cheng Jundong Li P. R. Hahn Huan Liu CML 19 168 0 25 Sep 2018
Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variables Rune Christiansen J. Peters CML 14 14 0 16 Aug 2018
Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms Adarsh Subbaswamy S. Saria OOD 8 25 0 09 Aug 2018
Causal Reasoning for Algorithmic Fairness Joshua R. Loftus Chris Russell Matt J. Kusner Ricardo M. A. Silva FaML CML 18 125 0 15 May 2018
A Constraint-Based Algorithm For Causal Discovery with Cycles, Latent Variables and Selection Bias Eric V. Strobl CML 13 30 0 05 May 2018
Causal Modeling of Dynamical Systems Stephan Bongers Tineke Blom Joris M. Mooij 15 22 0 23 Mar 2018
Causal Generative Neural Networks Olivier Goudet Diviyan Kalainathan Philippe Caillou Isabelle M Guyon David Lopez-Paz Michèle Sebag BDL CML DRL 27 58 0 24 Nov 2017
Learning Functional Causal Models with Generative Neural Networks Hugo Jair Escalante Sergio Escalera Xavier Baro Isabelle M Guyon Umut Güçlü Marcel van Gerven CML BDL 15 107 0 15 Sep 2017
Comparative Benchmarking of Causal Discovery Techniques Karamjit Singh Garima Gupta Vartika Tewari Gautam M. Shroff CML 8 12 0 18 Aug 2017
Fast Causal Inference with Non-Random Missingness by Test-Wise Deletion Eric V. Strobl Shyam Visweswaran Peter Spirtes CML 12 36 0 25 May 2017
Foundations of Structural Causal Models with Cycles and Latent Variables Stephan Bongers Patrick Forré J. Peters Joris M. Mooij 24 156 0 18 Nov 2016
algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD Joseph Ramsey Daniel Malinsky Kevin V. Bui CML 24 17 0 27 Jul 2016
Estimating and Controlling the False Discovery Rate for the PC Algorithm Using Edge-Specific P-Values Eric V. Strobl Peter Spirtes Shyam Visweswaran 24 18 0 14 Jul 2016
Structure Learning in Graphical Modeling Mathias Drton Marloes H. Maathuis CML 36 245 0 07 Jun 2016
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013) Tom Claassen Joris M. Mooij Tom Heskes CML 34 2 0 06 Nov 2014
Learning Bayesian Network Equivalence Classes with Ant Colony Optimization Rónán Daly Q. Shen 60 70 0 15 Jan 2014
Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedure Antti Hyttinen P. Hoyer F. Eberhardt Matti Järvisalo CML 34 89 0 26 Sep 2013
Learning Sparse Causal Models is not NP-hard Tom Claassen Joris Mooij Tom Heskes CML 51 117 0 26 Sep 2013
Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Models Peter Spirtes 40 28 0 17 Sep 2013
A generalized back-door criterion Marloes H. Maathuis Diego Colombo 51 34 0 22 Jul 2013
Reasoning about Independence in Probabilistic Models of Relational Data Marc E. Maier Katerina Marazopoulou David D. Jensen 32 34 0 18 Feb 2013
A Discovery Algorithm for Directed Cyclic Graphs Thomas S. Richardson CML 78 193 0 13 Feb 2013
A Bayesian Method for Causal Modeling and Discovery Under Selection G. Cooper CML 43 28 0 16 Jan 2013
Order-independent constraint-based causal structure learning Diego Colombo Marloes H. Maathuis CML 54 590 0 14 Nov 2012
A Bayesian Approach to Constraint Based Causal Inference Tom Claassen Tom Heskes TPM 57 98 0 16 Oct 2012
On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables F. Eberhardt Clark Glymour R. Scheines 44 152 0 04 Jul 2012
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables Ayesha R. Ali Thomas S. Richardson Peter Spirtes Jiji Zhang CML 53 42 0 04 Jul 2012
A theoretical study of Y structures for causal discovery S. Mani Peter Spirtes G. Cooper CML 45 64 0 27 Jun 2012
A Characterization of Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables Jiji Zhang 69 17 0 20 Jun 2012
On Identifying Total Effects in the Presence of Latent Variables and Selection bias Zhihong Cai Manabu Kuroki CML 48 47 0 13 Jun 2012
Noisy-OR Models with Latent Confounding Antti Hyttinen F. Eberhardt P. Hoyer CML 43 12 0 14 Feb 2012
A Logical Characterization of Constraint-Based Causal Discovery Tom Claassen Tom Heskes CML 47 38 0 14 Feb 2012
Learning high-dimensional directed acyclic graphs with latent and selection variables Diego Colombo Marloes H. Maathuis M. Kalisch Thomas S. Richardson CML 59 462 0 29 Apr 2011