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Differentiable Causal Discovery from Interventional Data

3 July 2020

Papers citing "Differentiable Causal Discovery from Interventional Data"

35 / 135 papers shown

Title
Large-Scale Differentiable Causal Discovery of Factor Graphs Romain Lopez Jan-Christian Hütter J. Pritchard Aviv Regev CML AI4CE 87 43 0 15 Jun 2022
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems Phillip Lippe Sara Magliacane Sindy Löwe Yuki M. Asano Taco S. Cohen E. Gavves CML 67 31 0 13 Jun 2022
On the Generalization and Adaption Performance of Causal Models Nino Scherrer Anirudh Goyal Stefan Bauer Yoshua Bengio Nan Rosemary Ke CML OOD BDL TTA 80 8 0 09 Jun 2022
Active Bayesian Causal Inference Christian Toth Lars Lorch Christian Knoll Andreas Krause Franz Pernkopf Robert Peharz Julius von Kügelgen 83 27 0 04 Jun 2022
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis Ronan Perry Julius von Kügelgen Bernhard Schölkopf 98 50 0 04 Jun 2022
BaCaDI: Bayesian Causal Discovery with Unknown Interventions Alexander Hagele Jonas Rothfuss Lars Lorch Vignesh Ram Somnath Bernhard Schölkopf Andreas Krause CML BDL 112 22 0 03 Jun 2022
Causal Structure Learning: a Combinatorial Perspective C. Squires Caroline Uhler CML 120 47 0 02 Jun 2022
PAC Generalization via Invariant Representations Advait Parulekar Karthikeyan Shanmugam Sanjay Shakkottai 91 4 0 30 May 2022
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models Erdun Gao Ignavier Ng Biwei Huang Li Shen Wei Huang Tongliang Liu Kun Zhang H. Bondell CML 137 23 0 27 May 2022
Amortized Inference for Causal Structure Learning Lars Lorch Scott Sussex Jonas Rothfuss Andreas Krause Bernhard Schölkopf CML 116 65 0 25 May 2022
Towards efficient representation identification in supervised learning Kartik Ahuja Divyat Mahajan Vasilis Syrgkanis Ioannis Mitliagkas CoGe OOD DRL 85 19 0 10 Apr 2022
Weakly supervised causal representation learning Johann Brehmer P. D. Haan Phillip Lippe Taco S. Cohen OOD CML 99 131 0 30 Mar 2022
Differentiable DAG Sampling Bertrand Charpentier Simon Kibler Stephan Günnemann 100 42 0 16 Mar 2022
Differentiable Causal Discovery Under Latent Interventions Gonccalo R. A. Faria André F. T. Martins Mário A. T. Figueiredo BDL CML OOD 89 23 0 04 Mar 2022
Interventions, Where and How? Experimental Design for Causal Models at Scale P. Tigas Yashas Annadani Andrew Jesson Bernhard Schölkopf Y. Gal Stefan Bauer CML 150 50 0 03 Mar 2022
Bayesian Structure Learning with Generative Flow Networks T. Deleu António Góis Chris C. Emezue M. Rankawat Simon Lacoste-Julien Stefan Bauer Yoshua Bengio BDL 109 157 0 28 Feb 2022
FedDAG: Federated DAG Structure Learning Erdun Gao Junjia Chen Li Shen Tongliang Liu Biwei Huang H. Bondell FedML 87 17 0 07 Dec 2021
gCastle: A Python Toolbox for Causal Discovery Keli Zhang Shengyu Zhu Marcus Kalander Ignavier Ng Junjian Ye Zhitang Chen Lujia Pan CML 127 61 0 30 Nov 2021
Scalable Intervention Target Estimation in Linear Models Burak Varici Karthikeyan Shanmugam P. Sattigeri A. Tajer CML 52 10 0 15 Nov 2021
Towards Federated Bayesian Network Structure Learning with Continuous Optimization Ignavier Ng Kun Zhang FedML 91 38 0 18 Oct 2021
Causal Explanations of Structural Causal Models Matej Zevcević Devendra Singh Dhami Constantin Rothkopf Kristian Kersting LRM 54 3 0 05 Oct 2021
Optimization-based Causal Estimation from Heterogenous Environments Mingzhang Yin Yixin Wang David M. Blei OOD 123 18 0 24 Sep 2021
Learning Neural Causal Models with Active Interventions Nino Scherrer O. Bilaniuk Yashas Annadani Anirudh Goyal Patrick Schwab Bernhard Schölkopf Michael C. Mozer Yoshua Bengio Stefan Bauer Nan Rosemary Ke CML 123 44 0 06 Sep 2021
Typing assumptions improve identification in causal discovery P. Brouillard Perouz Taslakian Alexandre Lacoste Sébastien Lachapelle Alexandre Drouin CML 84 13 0 22 Jul 2021
Efficient Neural Causal Discovery without Acyclicity Constraints Phillip Lippe Taco S. Cohen E. Gavves CML 99 72 0 22 Jul 2021
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA Sébastien Lachapelle Pau Rodríguez López Yash Sharma Katie Everett Rémi Le Priol Alexandre Lacoste Simon Lacoste-Julien CML OOD 104 141 0 21 Jul 2021
The Causal-Neural Connection: Expressiveness, Learnability, and Inference K. Xia Kai-Zhan Lee Yoshua Bengio Elias Bareinboim CML 81 111 0 02 Jul 2021
On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization Weilin Chen Jie Qiao Ruichu Cai Zijian Li CML 66 3 0 05 Jun 2021
Causal Graph Discovery from Self and Mutually Exciting Time Series S. Wei Yao Xie C. Josef Rishikesan Kamaleswaran CML 89 2 0 04 Jun 2021
DiBS: Differentiable Bayesian Structure Learning Lars Lorch Jonas Rothfuss Bernhard Schölkopf Andreas Krause 95 91 0 25 May 2021
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game Alexander G. Reisach C. Seiler S. Weichwald CML 85 142 0 26 Feb 2021
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models Matej Zečević Devendra Singh Dhami Athresh Karanam S. Natarajan Kristian Kersting CML TPM 89 33 0 20 Feb 2021
Causal Inference for Time series Analysis: Problems, Methods and Evaluation Raha Moraffah Paras Sheth Mansooreh Karami Anchit Bhattacharya Qianru Wang Anique Tahir A. Raglin Huan Liu CML AI4TS 113 110 0 11 Feb 2021
Inference of Causal Effects when Control Variables are Unknown Ludvig Hult Dave Zachariah CML 33 0 0 15 Dec 2020
On the Convergence of Continuous Constrained Optimization for Structure Learning Ignavier Ng Sébastien Lachapelle Nan Rosemary Ke Simon Lacoste-Julien Kun Zhang 105 38 0 23 Nov 2020