Learning Neural Causal Models with Active Interventions

6 September 2021

Stefan Bauer

Papers citing "Learning Neural Causal Models with Active Interventions"

33 / 33 papers shown

Title
IGDA: Interactive Graph Discovery through Large Language Model Agents Alex Havrilla David Alvarez-Melis Nicolò Fusi AI4CE 59 0 0 24 Feb 2025
BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery Chris Cundy Aditya Grover Stefano Ermon CML 70 73 0 06 Dec 2021
Efficient Neural Causal Discovery without Acyclicity Constraints Phillip Lippe Taco S. Cohen E. Gavves CML 38 73 0 22 Jul 2021
The Causal-Neural Connection: Expressiveness, Learnability, and Inference K. Xia Kai-Zhan Lee Yoshua Bengio Elias Bareinboim CML 57 109 0 02 Jul 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 53 48 0 14 Jun 2021
DiBS: Differentiable Bayesian Structure Learning Lars Lorch Jonas Rothfuss Bernhard Schölkopf Andreas Krause 39 87 0 25 May 2021
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery M. Vowels Necati Cihan Camgöz Richard Bowden CML 76 300 0 03 Mar 2021
Active Structure Learning of Causal DAGs via Directed Clique Tree C. Squires Sara Magliacane Kristjan Greenewald Dmitriy A. Katz Murat Kocaoglu Karthikeyan Shanmugam CML 80 34 0 01 Nov 2020
Differentiable Causal Discovery from Interventional Data P. Brouillard Sébastien Lachapelle Alexandre Lacoste Simon Lacoste-Julien Alexandre Drouin CML 44 186 0 03 Jul 2020
Active Invariant Causal Prediction: Experiment Selection through Stability Juan L. Gamella C. Heinze-Deml OOD 37 46 0 10 Jun 2020
Causality matters in medical imaging Daniel Coelho De Castro Ian Walker Ben Glocker CML 36 343 0 17 Dec 2019
A Graph Autoencoder Approach to Causal Structure Learning Ignavier Ng Shengyu Zhu Zhitang Chen Zhuangyan Fang BDL CML 44 82 0 18 Nov 2019
Interventional Experiment Design for Causal Structure Learning AmirEmad Ghassami Saber Salehkaleybar Negar Kiyavash CML 33 10 0 12 Oct 2019
Learning Neural Causal Models from Unknown Interventions Nan Rosemary Ke O. Bilaniuk Anirudh Goyal Stefan Bauer Hugo Larochelle Bernhard Schölkopf Michael C. Mozer C. Pal Yoshua Bengio CML OOD 86 168 0 02 Oct 2019
Learning Sparse Nonparametric DAGs Xun Zheng Chen Dan Bryon Aragam Pradeep Ravikumar Eric Xing CML 145 260 0 29 Sep 2019
Causal Discovery with Reinforcement Learning Shengyu Zhu Ignavier Ng Zhitang Chen CML 54 240 0 11 Jun 2019
Gradient-Based Neural DAG Learning Sébastien Lachapelle P. Brouillard T. Deleu Simon Lacoste-Julien BDL CML 48 273 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
Causal Discovery Toolbox: Uncover causal relationships in Python Diviyan Kalainathan Olivier Goudet CML 44 82 0 06 Mar 2019
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms Yoshua Bengio T. Deleu Nasim Rahaman Nan Rosemary Ke Sébastien Lachapelle O. Bilaniuk Anirudh Goyal C. Pal CML OOD 91 334 0 30 Jan 2019
Experimental Design for Cost-Aware Learning of Causal Graphs Erik M. Lindgren Murat Kocaoglu A. Dimakis S. Vishwanath CML 43 37 0 28 Oct 2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng Bryon Aragam Pradeep Ravikumar Eric Xing NoLa CML OffRL 74 937 0 04 Mar 2018
Budgeted Experiment Design for Causal Structure Learning AmirEmad Ghassami Saber Salehkaleybar Negar Kiyavash Elias Bareinboim CML 71 64 0 11 Sep 2017
Cost-Optimal Learning of Causal Graphs Murat Kocaoglu A. Dimakis S. Vishwanath CML 91 68 0 08 Mar 2017
Learning Causal Graphs with Small Interventions Karthikeyan Shanmugam Murat Kocaoglu A. Dimakis S. Vishwanath CML 89 106 0 30 Oct 2015
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 280 4,166 0 21 May 2015
Causal inference using invariant prediction: identification and confidence intervals J. Peters Peter Buhlmann N. Meinshausen OOD 107 966 0 06 Jan 2015
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data David Heckerman D. Geiger D. M. Chickering TPM 102 3,979 0 27 Feb 2013
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 86 153 0 04 Jul 2012
Almost Optimal Intervention Sets for Causal Discovery F. Eberhardt CML 92 59 0 13 Jun 2012
Identifiability of Causal Graphs using Functional Models J. Peters Joris Mooij Dominik Janzing Bernhard Schölkopf 84 155 0 14 Feb 2012
Searching for Bayesian Network Structures in the Space of Restricted Acyclic Partially Directed Graphs Silvia Acid L. M. D. Campos 155 133 0 30 Jun 2011
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs Alain Hauser Peter Buhlmann CML 84 425 0 14 Apr 2011