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Learning Representations for Counterfactual Inference

12 May 2016

Papers citing "Learning Representations for Counterfactual Inference"

50 / 403 papers shown

Title
Adversarial representation learning for synthetic replacement of private attributes John Martinsson Edvin Listo Zec D. Gillblad Olof Mogren PICV 39 8 0 14 Jun 2020
Learning Decomposed Representation for Counterfactual Inference Anpeng Wu Kun Kuang Junkun Yuan Bo Li Jianrong Tao Qiang Zhu Yueting Zhuang Leilei Gan CML 70 21 0 12 Jun 2020
Regret Minimization for Causal Inference on Large Treatment Space Akira Tanimoto Tomoya Sakai Takashi Takenouchi H. Kashima CML 58 10 0 10 Jun 2020
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains James Bannon Bradford T. Windsor Wenbo Song Tao Li CML OOD OffRL 73 20 0 03 Jun 2020
CausaLM: Causal Model Explanation Through Counterfactual Language Models Amir Feder Nadav Oved Uri Shalit Roi Reichart CML LRM 161 162 0 27 May 2020
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation Shonosuke Harada H. Kashima CML 18 3 0 11 May 2020
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates Katherine A. Keith David D. Jensen Brendan O'Connor CML 60 114 0 01 May 2020
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks Ankit Sharma Garima Gupta Ranjitha Prasad Arnab Chatterjee Lovekesh Vig Gautam M. Shroff CML 38 7 0 28 Apr 2020
Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects Will Y. Zou S. Shyam Michael Mui Mingshi Wang Jan Pedersen Zoubin Ghahramani CML 37 2 0 21 Apr 2020
Causality-aware counterfactual confounding adjustment for feature representations learned by deep models E. C. Neto AI4CE OOD BDL CML 66 2 0 20 Apr 2020
Estimating Individual Treatment Effects through Causal Populations Identification Céline Beji Michaël Bon Florian Yger Jamal Atif CML 14 3 0 10 Apr 2020
Learning Latent Causal Structures with a Redundant Input Neural Network Jonathan D. Young Bryan Andrews G. Cooper Xinghua Lu CML 36 8 0 29 Mar 2020
ParKCa: Causal Inference with Partially Known Causes Raquel Y. S. Aoki Martin Ester CML 43 5 0 17 Mar 2020
Optimizing Medical Treatment for Sepsis in Intensive Care: from Reinforcement Learning to Pre-Trial Evaluation Luchen Li I. Albert-Smet Aldo A. Faisal OffRL 65 10 0 13 Mar 2020
Adversarial Machine Learning: Bayesian Perspectives D. Insua Roi Naveiro Víctor Gallego Jason Poulos AAML 27 21 0 07 Mar 2020
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks Ioana Bica James Jordon M. Schaar CML 87 106 0 27 Feb 2020
Off-Policy Evaluation and Learning for External Validity under a Covariate Shift Masahiro Kato Masatoshi Uehara Shota Yasui OffRL 101 53 0 26 Feb 2020
MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models Imke Mayer Julie Josse Félix Raimundo Jean-Philippe Vert CML 66 12 0 25 Feb 2020
Causal Inference under Networked Interference and Intervention Policy Enhancement Yunpu Ma Volker Tresp CML 88 41 0 20 Feb 2020
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations Ioana Bica Ahmed Alaa James Jordon M. Schaar BDL CML 70 186 0 10 Feb 2020
A Survey on Causal Inference Liuyi Yao Zhixuan Chu Sheng Li Yaliang Li Jing Gao Aidong Zhang CML 114 514 0 05 Feb 2020
Treatment effect estimation with disentangled latent factors Weijia Zhang Lin Liu Jiuyong Li CML 87 89 0 29 Jan 2020
Causal query in observational data with hidden variables Debo Cheng Jiuyong Li Lin Liu Jixue Liu Kui Yu T. Le CML 80 11 0 28 Jan 2020
On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond Zifeng Wang Xi Chen Rui Wen Shao-Lun Huang 121 1 0 25 Jan 2020
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects Fredrik D. Johansson Uri Shalit Nathan Kallus David Sontag CML OOD 122 100 0 21 Jan 2020
The Counterfactual $χ$ -GAN A. Averitt Natnicha Vanitchanant Rajesh Ranganath A. Perotte CML BDL 19 8 0 09 Jan 2020
Artificial Intelligence for Social Good: A Survey Zheyuan Ryan Shi Claire Wang Fei Fang AI4TS 103 81 0 07 Jan 2020
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data Ruocheng Guo Jundong Li Huan Liu CML OffRL 85 21 0 22 Dec 2019
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation Zichen Zhang Qingfeng Lan Lei Ding Yue Wang Negar Hassanpour Russell Greiner BDL CML 53 9 0 19 Dec 2019
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population Ankit Sharma Garima Gupta Ranjitha Prasad Arnab Chatterjee Lovekesh Vig Gautam M. Shroff OOD 57 12 0 09 Dec 2019
Triply Robust Off-Policy Evaluation Anqi Liu Hao Liu Anima Anandkumar Yisong Yue OffRL 73 10 0 13 Nov 2019
MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming Yura N. Perov L. Graham Kostis Gourgoulias Jonathan G. Richens Ciarán M. Gilligan-Lee Adam Baker Saurabh Johri LRM 60 17 0 17 Oct 2019
Optimising Individual-Treatment-Effect Using Bandits Jeroen Berrevoets Sam Verboven Wouter Verbeke CML 20 3 0 16 Oct 2019
Estimation of Bounds on Potential Outcomes For Decision Making Maggie Makar Fredrik D. Johansson John Guttag David Sontag 13 1 0 10 Oct 2019
Conditional out-of-sample generation for unpaired data using trVAE M. Lotfollahi Mohsen Naghipourfar Fabian J. Theis F. A. Wolf GAN ViT DRL 129 19 0 04 Oct 2019
Representation Learning for Electronic Health Records W. Weng Peter Szolovits 76 19 0 19 Sep 2019
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models Yuta Saito Shota Yasui OOD CML 24 8 0 11 Sep 2019
Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective Tom Everitt Marcus Hutter Ramana Kumar Victoria Krakovna 101 97 0 13 Aug 2019
Quantifying Error in the Presence of Confounders for Causal Inference Rathin Desai Amit Sharma CML 13 0 0 10 Jul 2019
Learning Fair and Transferable Representations L. Oneto Michele Donini Andreas Maurer Massimiliano Pontil FaML 88 19 0 25 Jun 2019
Understanding artificial intelligence ethics and safety David Leslie FaML AI4TS 74 363 0 11 Jun 2019
Learning Individual Causal Effects from Networked Observational Data Ruocheng Guo Wenlin Yao Huan Liu CML OOD 72 97 0 08 Jun 2019
Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck Sungyub Kim Yong-Ho Baek Sung Ju Hwang Eunho Yang CML 21 1 0 07 Jun 2019
Adapting Neural Networks for the Estimation of Treatment Effects Claudia Shi David M. Blei Victor Veitch CML 152 376 0 05 Jun 2019
On the Fairness of Disentangled Representations Francesco Locatello G. Abbati Tom Rainforth Stefan Bauer Bernhard Schölkopf Olivier Bachem FaML DRL 79 227 0 31 May 2019
Deep Generalized Method of Moments for Instrumental Variable Analysis Andrew Bennett Nathan Kallus Tobias Schnabel 68 128 0 29 May 2019
Matching on What Matters: A Pseudo-Metric Learning Approach to Matching Estimation in High Dimensions Gentry Johnson B. Quistorff Matt Goldman 18 0 0 28 May 2019
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices Jessica Morley Luciano Floridi Libby Kinsey Anat Elhalal 73 57 0 15 May 2019
Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models Michael Oberst David Sontag CML OffRL 83 174 0 14 May 2019
Combining Parametric and Nonparametric Models for Off-Policy Evaluation Omer Gottesman Yao Liu Scott Sussex Emma Brunskill Finale Doshi-Velez OffRL 105 36 0 14 May 2019