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Learning Representations for Counterfactual Inference

12 May 2016

Papers citing "Learning Representations for Counterfactual Inference"

50 / 414 papers shown

Title
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge Abhin Shah Karthikeyan Shanmugam Kartik Ahuja CML 100 13 0 22 Jun 2021
Costs and Benefits of Fair Regression Han Zhao FaML 80 10 0 16 Jun 2021
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness Adam Foster Árpi Vezér C. A. Glastonbury Páidí Creed Sam Abujudeh Aaron Sim FaML 117 6 0 15 Jun 2021
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation Liyuan Xu Heishiro Kanagawa Arthur Gretton CML 157 38 0 07 Jun 2021
Counterfactual Maximum Likelihood Estimation for Training Deep Networks Xinyi Wang Wenhu Chen Michael Stephen Saxon Wenjie Wang OOD CML BDL 154 8 0 07 Jun 2021
On Inductive Biases for Heterogeneous Treatment Effect Estimation Alicia Curth M. Schaar CML 253 88 0 07 Jun 2021
Learning from Counterfactual Links for Link Prediction Tong Zhao Gang Liu Daheng Wang Wenhao Yu Meng Jiang CML OOD 140 104 0 03 Jun 2021
Causal Effect Inference for Structured Treatments Jean Kaddour Yuchen Zhu Qi Liu Matt J. Kusner Ricardo M. A. Silva CML 312 52 0 03 Jun 2021
Causally motivated Shortcut Removal Using Auxiliary Labels Maggie Makar Ben Packer D. Moldovan Davis W. Blalock Yoni Halpern Alexander DÁmour OOD CML 141 80 0 13 May 2021
Neural Networks for Learning Counterfactual G-Invariances from Single Environments S Chandra Mouli Bruno Ribeiro OOD 115 13 0 20 Apr 2021
Sequential Deconfounding for Causal Inference with Unobserved Confounders Tobias Hatt Stefan Feuerriegel CML 179 28 0 16 Apr 2021
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap Alexander DÁmour Alexander M. Franks CML 82 11 0 12 Apr 2021
Matched sample selection with GANs for mitigating attribute confounding Chandan Singh Guha Balakrishnan Pietro Perona GAN 81 6 0 24 Mar 2021
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments S. Parbhoo Stefan Bauer Patrick Schwab CML BDL 97 18 0 20 Mar 2021
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments Lizhen Nie Mao Ye Qiang Liu D. Nicolae CML 89 74 0 14 Mar 2021
Treatment Effect Estimation using Invariant Risk Minimization Abhin Shah Kartik Ahuja Karthikeyan Shanmugam Dennis L. Wei Kush R. Varshney Amit Dhurandhar CML OOD 84 2 0 13 Mar 2021
Limitations of Post-Hoc Feature Alignment for Robustness Collin Burns Jacob Steinhardt OOD 112 23 0 10 Mar 2021
Size-Invariant Graph Representations for Graph Classification Extrapolations Beatrice Bevilacqua Yangze Zhou Bruno Ribeiro OOD 170 113 0 08 Mar 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression Junhyung Park Uri Shalit Bernhard Schölkopf Krikamol Muandet CML 154 35 0 16 Feb 2021
Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation A. Caron G. Baio I. Manolopoulou CML 107 19 0 12 Feb 2021
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge Trent Kyono Ioana Bica Zhaozhi Qian Mihaela van der Schaar OOD CML 77 7 0 11 Feb 2021
Generating Synthetic Text Data to Evaluate Causal Inference Methods Zach Wood-Doughty I. Shpitser Mark Dredze SyDa CML 106 12 0 10 Feb 2021
Grab the Reins of Crowds: Estimating the Effects of Crowd Movement Guidance Using Causal Inference Koh Takeuchi Ryo Nishida Hisashi Kashima Masaki Onishi CML 59 4 0 08 Feb 2021
Learning Matching Representations for Individualized Organ Transplantation Allocation Can Xu Ahmed Alaa Ioana Bica B. Ershoff M. Cannesson M. Schaar OOD 70 7 0 28 Jan 2021
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms Alicia Curth M. Schaar CML 231 156 0 26 Jan 2021
Estimating Average Treatment Effects via Orthogonal Regularization Tobias Hatt Stefan Feuerriegel CML 309 36 0 21 Jan 2021
Model Compression for Domain Adaptation through Causal Effect Estimation Guy Rotman Amir Feder Roi Reichart CML 106 8 0 18 Jan 2021
Intact-VAE: Estimating Treatment Effects under Unobserved Confounding Pengzhou (Abel) Wu Kenji Fukumizu CML 131 13 0 17 Jan 2021
Fairness in Machine Learning L. Oneto Silvia Chiappa FaML 405 511 0 31 Dec 2020
CAMTA: Causal Attention Model for Multi-touch Attribution Sachin Kumar Garima Gupta Ranjitha Prasad Arnab Chatterjee Lovekesh Vig Gautam M. Shroff CML HAI 95 12 0 21 Dec 2020
Fundamental Limits and Tradeoffs in Invariant Representation Learning Han Zhao Chen Dan Bryon Aragam Tommi Jaakkola Geoffrey J. Gordon Pradeep Ravikumar FaML 181 46 0 19 Dec 2020
Treatment Targeting by AUUC Maximization with Generalization Guarantees Artem Betlei Eustache Diemert Massih-Reza Amini CML 81 5 0 17 Dec 2020
The Causal Learning of Retail Delinquency Yiyan Huang Cheuk Hang Leung Xing Yan Qi Wu Nanbo Peng DongDong Wang Zhixiang Huang CML 90 9 0 17 Dec 2020
Causality-Aware Neighborhood Methods for Recommender Systems Masahiro Sato S. Takemori Janmajay Singh Qian Zhang BDL CML 87 7 0 17 Dec 2020
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models Dong Wang Yuewei Yang Chenyang Tao Zhe Gan Liqun Chen Fanjie Kong Ricardo Henao Lawrence Carin 109 0 0 06 Dec 2020
Balance Regularized Neural Network Models for Causal Effect Estimation Mehrdad Farajtabar Andrew Lee Yuanjian Feng Vishal Gupta Peter Dolan Harish Chandran M. Szummer CML 82 6 0 23 Nov 2020
Confounding Feature Acquisition for Causal Effect Estimation Shirly Wang S. Yi Shalmali Joshi Marzyeh Ghassemi CML 53 2 0 17 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 106 6 0 09 Nov 2020
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks Minshuo Chen Hao Liu Wenjing Liao T. Zhao CML OOD OffRL 64 7 0 03 Nov 2020
Adapting Neural Networks for Uplift Models Mouloud Belbahri Olivier Gandouet Ghaith Kazma 62 12 0 30 Oct 2020
DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data Yanbo Xu Cao Xiao Jimeng Sun BDL OOD CML 40 1 0 28 Oct 2020
Counterfactual Representation Learning with Balancing Weights Serge Assaad Shuxi Zeng Chenyang Tao Shounak Datta Nikhil Mehta Ricardo Henao Fan Li Lawrence Carin CML OOD 214 68 0 23 Oct 2020
Poincare: Recommending Publication Venues via Treatment Effect Estimation Ryoma Sato M. Yamada H. Kashima CML 84 2 0 19 Oct 2020
Double Robust Representation Learning for Counterfactual Prediction Shuxi Zeng Serge Assaad Chenyang Tao Shounak Datta Lawrence Carin Fan Li OOD CML 105 6 0 15 Oct 2020
GraphITE: Estimating Individual Effects of Graph-structured Treatments Shonosuke Harada H. Kashima CML 143 25 0 29 Sep 2020
Learning from eXtreme Bandit Feedback Romain Lopez Inderjit S. Dhillon Michael I. Jordan OffRL 120 25 0 27 Sep 2020
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference Galen Cassebeer Weld Peter West M. Glenski David Arbour Ryan Rossi Tim Althoff CML 196 22 0 21 Sep 2020
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review A. Caron G. Baio I. Manolopoulou CML 117 64 0 14 Sep 2020
Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback Zifeng Wang Xi Chen Rui Wen Shao-Lun Huang E. Kuruoglu Yefeng Zheng BDL CML OffRL 121 85 0 06 Sep 2020
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals Otmane Sakhi Stephen Bonner D. Rohde Flavian Vasile 121 37 0 28 Aug 2020