Causal Effect Inference with Deep Latent-Variable Models

24 May 2017

Joris Mooij

Papers citing "Causal Effect Inference with Deep Latent-Variable Models"

50 / 401 papers shown

Title
SAG-VAE: End-to-end Joint Inference of Data Representations and Feature Relations Chen Wang Chengyuan Deng Vladimir A. Ivanov GNN DRL 19 6 0 27 Nov 2019
Bayesian causal inference via probabilistic program synthesis Sam Witty Alexander K. Lew David D. Jensen Vikash K. Mansinghka 9 3 0 30 Oct 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 37 6,110 0 22 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 17 19 0 04 Oct 2019
Task-Relevant Adversarial Imitation Learning Konrad Zolna Scott E. Reed Alexander Novikov Sergio Gomez Colmenarejo David Budden Serkan Cabi Misha Denil Nando de Freitas Ziyun Wang GAN 22 61 0 02 Oct 2019
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models Yuta Saito Shota Yasui OOD CML 8 8 0 11 Sep 2019
Off-Policy Evaluation in Partially Observable Environments Guy Tennenholtz Shie Mannor Uri Shalit OffRL 14 85 0 09 Sep 2019
Estimating Granger Causality with Unobserved Confounders via Deep Latent-Variable Recurrent Neural Network Yuan Meng CML BDL 10 3 0 09 Sep 2019
Probabilistic Models with Deep Neural Networks A. Masegosa Rafael Cabañas H. Langseth Thomas D. Nielsen Antonio Salmerón BDL 6 12 0 09 Aug 2019
Policy Evaluation with Latent Confounders via Optimal Balance Andrew Bennett Nathan Kallus CML 16 18 0 06 Aug 2019
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems Shalmali Joshi Oluwasanmi Koyejo Warut D. Vijitbenjaronk Been Kim Joydeep Ghosh FaML 17 184 0 22 Jul 2019
Counterfactual Reasoning for Fair Clinical Risk Prediction Stephen R. Pfohl Tony Duan D. Ding N. Shah OOD CML 25 57 0 14 Jul 2019
Environment Reconstruction with Hidden Confounders for Reinforcement Learning based Recommendation Wenjie Shang Yang Yu Qingyang Li Zhiwei Qin Yiping Meng Jieping Ye CML 14 51 0 12 Jul 2019
General Control Functions for Causal Effect Estimation from Instrumental Variables A. Puli Rajesh Ranganath CML 16 4 0 08 Jul 2019
Identify treatment effect patterns for personalised decisions Jiuyong Li Lin Liu Yizhao Han Saisai Ma T. Le Jixue Liu CML 14 1 0 14 Jun 2019
Learning Individual Causal Effects from Networked Observational Data Ruocheng Guo Wenlin Yao Huan Liu CML OOD 19 96 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 12 1 0 07 Jun 2019
Adapting Neural Networks for the Estimation of Treatment Effects Claudia Shi David M. Blei Victor Veitch CML 19 357 0 05 Jun 2019
Adapting Text Embeddings for Causal Inference Victor Veitch Dhanya Sridhar David M. Blei CML 14 21 0 29 May 2019
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines Chirag Nagpal Dennis L. Wei B. Vinzamuri Monica Shekhar Sara E. Berger Subhro Das Kush R. Varshney CML 11 25 0 08 May 2019
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data Xin Du Lei Sun W. Duivesteijn Alexander G. Nikolaev Mykola Pechenizkiy OOD CML 18 41 0 30 Apr 2019
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records Linying Zhang Yixin Wang A. Ostropolets J. J. Mulgrave David M. Blei G. Hripcsak BDL CML 15 1 0 03 Apr 2019
Elements of Sequential Monte Carlo C. A. Naesseth Fredrik Lindsten Thomas B. Schon 24 95 0 12 Mar 2019
On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives Alexander DÁmour CML 19 41 0 27 Feb 2019
Using Embeddings to Correct for Unobserved Confounding in Networks Victor Veitch Yixin Wang David M. Blei CML 15 56 0 11 Feb 2019
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves Patrick Schwab Lorenz Linhardt Stefan Bauer J. M. Buhmann W. Karlen CML OOD 24 131 0 03 Feb 2019
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders Ioana Bica Ahmed Alaa M. Schaar BDL CML AI4TS 14 111 0 01 Feb 2019
Learning Interpretable Models with Causal Guarantees Carolyn Kim Osbert Bastani FaML OOD CML 14 17 0 24 Jan 2019
Deconfounding Reinforcement Learning in Observational Settings Chaochao Lu Bernhard Schölkopf José Miguel Hernández-Lobato CML OOD 23 72 0 26 Dec 2018
Learning Controllable Fair Representations Jiaming Song Pratyusha Kalluri Aditya Grover Shengjia Zhao Stefano Ermon FaML 17 176 0 11 Dec 2018
A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis Cédric Beaulac Jeffrey S. Rosenthal D. Hodgson BDL DRL CML 11 2 0 29 Nov 2018
Estimating Causal Effects With Partial Covariates For Clinical Interpretability S. Parbhoo Mario Wieser Volker Roth CML 6 0 0 26 Nov 2018
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning Changhee Lee Nicholas Mastronarde M. Schaar FedML CML 11 17 0 21 Nov 2018
Adversarial Balancing for Causal Inference Michal Ozery-Flato Pierre Thodoroff Matan Ninio Michal Rosen-Zvi T. El-Hay CML GAN 13 25 0 17 Oct 2018
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks Patrick Schwab Lorenz Linhardt W. Karlen CML BDL 13 111 0 01 Oct 2018
A Survey of Learning Causality with Data: Problems and Methods Ruocheng Guo Lu Cheng Jundong Li P. R. Hahn Huan Liu CML 32 168 0 25 Sep 2018
Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data David Madras Elliot Creager T. Pitassi R. Zemel FaML 6 132 0 07 Sep 2018
Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects Vineeth Rakesh Ruocheng Guo Raha Moraffah Nitin Agarwal Huan Liu CML BDL 15 43 0 09 Aug 2018
Cause-Effect Deep Information Bottleneck For Systematically Missing Covariates S. Parbhoo Mario Wieser Aleksander Wieczorek Volker Roth CML 12 5 0 06 Jul 2018
Causal Inference with Noisy and Missing Covariates via Matrix Factorization Nathan Kallus Xiaojie Mao Madeleine Udell CML 6 62 0 03 Jun 2018
Multiple Causal Inference with Latent Confounding Rajesh Ranganath A. Perotte CML 6 50 0 21 May 2018
The Blessings of Multiple Causes Yixin Wang David M. Blei AI4CE CML 24 284 0 17 May 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 17 217 0 20 Mar 2018
Distribution Matching in Variational Inference Mihaela Rosca Balaji Lakshminarayanan S. Mohamed GAN CML DRL 32 98 0 19 Feb 2018
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training Nathan Kallus CML OOD 21 74 0 15 Feb 2018
More Robust Doubly Robust Off-policy Evaluation Mehrdad Farajtabar Yinlam Chow Mohammad Ghavamzadeh OffRL 13 264 0 10 Feb 2018
Deep Learning for Genomics: A Concise Overview Tianwei Yue Yuanxin Wang Longxiang Zhang Chunming Gu Haohan Wang Wenping Wang Qi Lyu Yujie Dun AILaw VLM BDL 30 89 0 02 Feb 2018
Conditional Variance Penalties and Domain Shift Robustness C. Heinze-Deml N. Meinshausen OOD VLM 31 4 0 31 Oct 2017
Implicit Causal Models for Genome-wide Association Studies Dustin Tran David M. Blei CML 18 43 0 30 Oct 2017
Optimal Auctions through Deep Learning: Advances in Differentiable Economics Paul Dutting Zhe Feng Harikrishna Narasimhan David C. Parkes S. Ravindranath 15 34 0 12 Jun 2017