v1v2v3 (latest)

Causal inference using invariant prediction: identification and confidence intervals

6 January 2015

Papers citing "Causal inference using invariant prediction: identification and confidence intervals"

50 / 493 papers shown

Title
Explainability-aided Domain Generalization for Image Classification Robin M. Schmidt FAtt OOD 51 1 0 05 Apr 2021
Improved and efficient inter-vehicle distance estimation using road gradients of both ego and target vehicles Robik Shrestha Jinkyu Lee Kushal Kafle S. Hwang Il Yong Chun 64 1 0 01 Apr 2021
Learning Under Adversarial and Interventional Shifts Harvineet Singh Shalmali Joshi Finale Doshi-Velez Himabindu Lakkaraju OOD 58 3 0 29 Mar 2021
Meta-Learned Invariant Risk Minimization Jun-Hyun Bae Inchul Choi Minho Lee OOD 59 11 0 24 Mar 2021
An Empirical Framework for Domain Generalization in Clinical Settings Haoran Zhang Natalie Dullerud Laleh Seyyed-Kalantari Q. Morris Shalmali Joshi Marzyeh Ghassemi OOD AI4CE 125 61 0 20 Mar 2021
Regularizing towards Causal Invariance: Linear Models with Proxies Michael Oberst Nikolaj Thams J. Peters David Sontag OOD 75 25 0 03 Mar 2021
Audio scene monitoring using redundant ad-hoc microphone array networks Peter Gerstoft Yihan Hu Michael J. Bianco Chaitanya Patil Ardel Alegre Y. Freund François Grondin 73 12 0 02 Mar 2021
Scalable Causal Domain Adaptation Mohammad Ali Javidian O. Pandey Pooyan Jamshidi CML 31 4 0 27 Feb 2021
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization Elan Rosenfeld Pradeep Ravikumar Andrej Risteski 102 36 0 25 Feb 2021
Nonlinear Invariant Risk Minimization: A Causal Approach Chaochao Lu Yuhuai Wu Jośe Miguel Hernández-Lobato Bernhard Schölkopf CML OOD 128 52 0 24 Feb 2021
Towards Causal Representation Learning Bernhard Schölkopf Francesco Locatello Stefan Bauer Nan Rosemary Ke Nal Kalchbrenner Anirudh Goyal Yoshua Bengio OOD CML AI4CE 152 322 0 22 Feb 2021
Linear unit-tests for invariance discovery Benjamin Aubin A. Slowik Martín Arjovsky Léon Bottou David Lopez-Paz OOD 67 31 0 22 Feb 2021
On Calibration and Out-of-domain Generalization Yoav Wald Amir Feder D. Greenfeld Uri Shalit OODD 123 158 0 20 Feb 2021
Model-Invariant State Abstractions for Model-Based Reinforcement Learning Manan Tomar Amy Zhang Roberto Calandra Matthew E. Taylor Joelle Pineau 92 26 0 19 Feb 2021
Out-of-Distribution Generalization Analysis via Influence Function Haotian Ye Chuanlong Xie Yue Liu Zhenguo Li OOD 50 13 0 21 Jan 2021
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis A. Lundborg Rajen Dinesh Shah J. Peters 74 23 0 18 Jan 2021
Learning and scoring Gaussian latent variable causal models with unknown additive interventions Armeen Taeb Juan L. Gamella C. Heinze-Deml Peter Buhlmann CML 122 2 0 18 Jan 2021
Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fix Ruocheng Guo Pengchuan Zhang Hao Liu Emre Kıcıman OOD 80 37 0 16 Jan 2021
Does Invariant Risk Minimization Capture Invariance? Pritish Kamath Akilesh Tangella Danica J. Sutherland Nathan Srebro OOD 279 128 0 04 Jan 2021
Generative Interventions for Causal Learning Chengzhi Mao Augustine Cha Amogh Gupta Hongya Wang Junfeng Yang Carl Vondrick CML OOD 79 66 0 22 Dec 2020
WILDS: A Benchmark of in-the-Wild Distribution Shifts Pang Wei Koh Shiori Sagawa Henrik Marklund Sang Michael Xie Marvin Zhang ... A. Kundaje Emma Pierson Sergey Levine Chelsea Finn Percy Liang OOD 277 1,451 0 14 Dec 2020
On the Binding Problem in Artificial Neural Networks Klaus Greff Sjoerd van Steenkiste Jürgen Schmidhuber OCL 308 267 0 09 Dec 2020
Intervention Design for Effective Sim2Real Transfer Melissa Mozifian Amy Zhang Joelle Pineau David Meger 43 9 0 03 Dec 2020
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer Zidi Xiu Junya Chen Ricardo Henao B. Goldstein Lawrence Carin Chenyang Tao 47 10 0 25 Nov 2020
Invariant Representation Learning for Treatment Effect Estimation Claudia Shi Victor Veitch David M. Blei OOD CML 47 31 0 24 Nov 2020
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies Zijian Guo 56 17 0 15 Nov 2020
Fairness and Robustness in Invariant Learning: A Case Study in Toxicity Classification Robert Adragna Elliot Creager David Madras R. Zemel OOD FaML 74 43 0 12 Nov 2020
Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners E. C. Neto OOD CML 61 6 0 09 Nov 2020
Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features E. C. Neto Phil Snyder S. Sieberts L. Omberg CML OOD 6 1 0 09 Nov 2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander DÁmour Katherine A. Heller D. Moldovan Ben Adlam B. Alipanahi ... Kellie Webster Steve Yadlowsky T. Yun Xiaohua Zhai D. Sculley OffRL 153 688 0 06 Nov 2020
Causal Autoregressive Flows Ilyes Khemakhem R. Monti R. Leech Aapo Hyvarinen CML OOD AI4CE 107 111 0 04 Nov 2020
Latent Causal Invariant Model Xinwei Sun Botong Wu Xiangyu Zheng Chang-Shu Liu Wei Chen Tao Qin Tie-Yan Liu OOD CML BDL 88 14 0 04 Nov 2020
Learning Causal Semantic Representation for Out-of-Distribution Prediction Chang-Shu Liu Xinwei Sun Jindong Wang Haoyue Tang Tao Li Tao Qin Wei Chen Tie-Yan Liu CML OODD OOD 127 106 0 03 Nov 2020
Understanding the Failure Modes of Out-of-Distribution Generalization Vaishnavh Nagarajan Anders Andreassen Behnam Neyshabur OOD OODD 77 178 0 29 Oct 2020
Domain adaptation under structural causal models Yuansi Chen Peter Buhlmann CML OOD AI4CE 132 40 0 29 Oct 2020
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions Kartik Ahuja Karthikeyan Shanmugam Amit Dhurandhar 57 9 0 28 Oct 2020
Causal Transfer Random Forest: Combining Logged Data and Randomized Experiments for Robust Prediction Shuxi Zeng Murat Ali Bayir Joel Pfeiffer Denis Xavier Charles Emre Kıcıman TTA 41 18 0 17 Oct 2020
Representation Learning via Invariant Causal Mechanisms Jovana Mitrović Brian McWilliams Jacob Walker Lars Buesing Charles Blundell CML OOD SSL 77 250 0 15 Oct 2020
Environment Inference for Invariant Learning Elliot Creager J. Jacobsen R. Zemel OOD 68 384 0 14 Oct 2020
The Risks of Invariant Risk Minimization Elan Rosenfeld Pradeep Ravikumar Andrej Risteski OOD 98 312 0 12 Oct 2020
Social Commonsense Reasoning with Multi-Head Knowledge Attention Debjit Paul Anette Frank LRM 31 19 0 12 Oct 2020
Causal learning with sufficient statistics: an information bottleneck approach D. Chicharro M. Besserve S. Panzeri CML 52 5 0 12 Oct 2020
Explaining The Efficacy of Counterfactually Augmented Data Divyansh Kaushik Amrith Rajagopal Setlur Eduard H. Hovy Zachary Chase Lipton CML 72 82 0 05 Oct 2020
CASTLE: Regularization via Auxiliary Causal Graph Discovery Trent Kyono Yao Zhang M. Schaar OOD CML 66 69 0 28 Sep 2020
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks Keyulu Xu Mozhi Zhang Jingling Li S. Du Ken-ichi Kawarabayashi Stefanie Jegelka MLT 182 313 0 24 Sep 2020
Causal Bandits without prior knowledge using separating sets A. D. Kroon Danielle Belgrave Joris M. Mooij CML 30 23 0 16 Sep 2020
Learning explanations that are hard to vary Giambattista Parascandolo Alexander Neitz Antonio Orvieto Luigi Gresele Bernhard Schölkopf FAtt 86 187 0 01 Sep 2020
When is invariance useful in an Out-of-Distribution Generalization problem ? Masanori Koyama Shoichiro Yamaguchi OOD 100 67 0 04 Aug 2020
Out-of-distribution Generalization via Partial Feature Decorrelation Xin Guo Zhengxu Yu Chao Xiang Zhongming Jin Jianqiang Huang Deng Cai Xiaofei He Xiansheng Hua OODD OOD 20 0 0 30 Jul 2020
Information-Theoretic Approximation to Causal Models Peter Gmeiner 14 0 0 29 Jul 2020