Causal inference using invariant prediction: identification and confidence intervals

6 January 2015

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

50 / 206 papers shown

Title
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 39 11 0 02 Mar 2021
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization Elan Rosenfeld Pradeep Ravikumar Andrej Risteski 34 34 0 25 Feb 2021
On Calibration and Out-of-domain Generalization Yoav Wald Amir Feder D. Greenfeld Uri Shalit OODD 30 153 0 20 Feb 2021
Model-Invariant State Abstractions for Model-Based Reinforcement Learning Manan Tomar Amy Zhang Roberto Calandra Matthew E. Taylor Joelle Pineau 27 24 0 19 Feb 2021
Does Invariant Risk Minimization Capture Invariance? Pritish Kamath Akilesh Tangella Danica J. Sutherland Nathan Srebro OOD 215 126 0 04 Jan 2021
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 108 1,386 0 14 Dec 2020
On the Binding Problem in Artificial Neural Networks Klaus Greff Sjoerd van Steenkiste Jürgen Schmidhuber OCL 233 255 0 09 Dec 2020
Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies Zijian Guo 30 17 0 15 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 32 6 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 77 671 0 06 Nov 2020
Causal Autoregressive Flows Ilyes Khemakhem R. Monti R. Leech Aapo Hyvarinen CML OOD AI4CE 27 108 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 31 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 35 104 0 03 Nov 2020
Domain adaptation under structural causal models Yuansi Chen Peter Buhlmann CML OOD AI4CE 39 39 0 29 Oct 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 32 306 0 24 Sep 2020
Learning explanations that are hard to vary Giambattista Parascandolo Alexander Neitz Antonio Orvieto Luigi Gresele Bernhard Schölkopf FAtt 29 179 0 01 Sep 2020
When is invariance useful in an Out-of-Distribution Generalization problem ? Masanori Koyama Shoichiro Yamaguchi OOD 38 65 0 04 Aug 2020
Distributionally Robust Losses for Latent Covariate Mixtures John C. Duchi Tatsunori Hashimoto Hongseok Namkoong 18 79 0 28 Jul 2020
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations Zhun Deng Frances Ding Cynthia Dwork Rachel Hong Giovanni Parmigiani Prasad Patil Pragya Sur OOD FaML 19 29 0 20 Jun 2020
Self-training Avoids Using Spurious Features Under Domain Shift Yining Chen Colin Wei Ananya Kumar Tengyu Ma OOD 29 85 0 17 Jun 2020
Risk Variance Penalization Chuanlong Xie Haotian Ye Fei Chen Yue Liu Rui Sun Zhenguo Li 61 33 0 13 Jun 2020
Domain Generalization using Causal Matching Divyat Mahajan Shruti Tople Amit Sharma OOD 38 326 0 12 Jun 2020
Conformal Inference of Counterfactuals and Individual Treatment Effects Lihua Lei Emmanuel J. Candès CML 23 190 0 11 Jun 2020
Active Invariant Causal Prediction: Experiment Selection through Stability Juan L. Gamella C. Heinze-Deml OOD 21 45 0 10 Jun 2020
Balance-Subsampled Stable Prediction Kun Kuang Hengtao Zhang Fei Wu Yueting Zhuang Aijun Zhang OOD 16 3 0 08 Jun 2020
Distributional robustness of K-class estimators and the PULSE M. E. Jakobsen J. Peters OOD 11 29 0 07 May 2020
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision Damien Teney Ehsan Abbasnejad Anton Van Den Hengel OOD SSL CML 42 118 0 20 Apr 2020
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder Mengyue Yang Furui Liu Zhitang Chen Xinwei Shen Jianye Hao Jun Wang OOD CoGe CML 41 44 0 18 Apr 2020
Out-of-Distribution Generalization via Risk Extrapolation (REx) David M. Krueger Ethan Caballero J. Jacobsen Amy Zhang Jonathan Binas Dinghuai Zhang Rémi Le Priol Aaron Courville OOD 215 908 0 02 Mar 2020
Invariant Risk Minimization Games Kartik Ahuja Karthikeyan Shanmugam Kush R. Varshney Amit Dhurandhar OOD 33 244 0 11 Feb 2020
Stable Prediction with Model Misspecification and Agnostic Distribution Shift Kun Kuang Ruoxuan Xiong Peng Cui Susan Athey Yue Liu OOD 30 128 0 31 Jan 2020
Improving Model Robustness Using Causal Knowledge T. Kyono M. Schaar OOD 30 12 0 27 Nov 2019
Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization Shiori Sagawa Pang Wei Koh Tatsunori B. Hashimoto Percy Liang OOD 16 1,202 0 20 Nov 2019
Causality-based Feature Selection: Methods and Evaluations Kui Yu Xianjie Guo Lin Liu Jiuyong Li Hao Wang Zhaolong Ling Xindong Wu CML 29 92 0 17 Nov 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 44 168 0 02 Oct 2019
Alleviating Privacy Attacks via Causal Learning Shruti Tople Amit Sharma A. Nori MIACV OOD 33 32 0 27 Sep 2019
Invariant Risk Minimization Martín Arjovsky Léon Bottou Ishaan Gulrajani David Lopez-Paz OOD 101 2,165 0 05 Jul 2019
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models Erdun Gao Kun Zhang Biwei Huang Clark Glymour CML AI4TS 29 63 0 26 May 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 51 332 0 30 Jan 2019
Veridical Data Science Bin Yu Karl Kumbier 23 162 0 23 Jan 2019
Learning stable and predictive structures in kinetic systems: Benefits of a causal approach Niklas Pfister Stefan Bauer J. Peters CML 22 41 0 28 Oct 2018
A Survey of Learning Causality with Data: Problems and Methods Ruocheng Guo Lu Cheng Jundong Li P. R. Hahn Huan Liu CML 39 168 0 25 Sep 2018
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models Alexander Neitz Giambattista Parascandolo Stefan Bauer Bernhard Schölkopf 31 38 0 14 Aug 2018
Stable Prediction across Unknown Environments Kun Kuang Ruoxuan Xiong Peng Cui Susan Athey Bo Li OOD 18 166 0 16 Jun 2018
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise Niklas Pfister S. Weichwald Peter Buhlmann Bernhard Schölkopf OOD CML 18 16 0 04 Jun 2018
The Blessings of Multiple Causes Yixin Wang David M. Blei AI4CE CML 24 284 0 17 May 2018
Two-sample instrumental variable analyses using heterogeneous samples Qingyuan Zhao Jingshu Wang J. Bowden Dylan S. Small 16 46 0 31 Aug 2017
Learning Causal Structures Using Regression Invariance AmirEmad Ghassami Saber Salehkaleybar Negar Kiyavash Kun Zhang OOD CML 27 60 0 26 May 2017
Group invariance principles for causal generative models M. Besserve Naji Shajarisales Bernhard Schölkopf Dominik Janzing 16 48 0 05 May 2017
Cost-Optimal Learning of Causal Graphs Murat Kocaoglu A. Dimakis S. Vishwanath CML 18 67 0 08 Mar 2017