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
Estimating Categorical Counterfactuals via Deep Twin Networks Athanasios Vlontzos Bernhard Kainz Ciarán M. Gilligan-Lee OOD CML BDL 26 16 0 04 Sep 2021
DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data N. Alipourfard Keith Burghardt Kristina Lerman 19 0 0 31 Aug 2021
E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling Javier Albert Dmitri Goldenberg 13 22 0 11 Aug 2021
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators Alicia Curth M. Schaar CML 14 7 0 28 Jul 2021
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions Eike Petersen Yannik Potdevin Esfandiar Mohammadi Stephan Zidowitz Sabrina Breyer ... Sandra Henn Ludwig Pechmann M. Leucker P. Rostalski Christian Herzog FaML AILaw OOD 32 21 0 20 Jul 2021
The Causal-Neural Connection: Expressiveness, Learnability, and Inference K. Xia Kai-Zhan Lee Yoshua Bengio Elias Bareinboim CML 28 104 0 02 Jul 2021
CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning Daniel J. McDuff Yale Song Jiyoung Lee Vibhav Vineet Sai H. Vemprala N. Gyde Hadi Salman Shuang Ma Kwanghoon Sohn Ashish Kapoor CML 33 28 0 25 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 9 5 0 15 Jun 2021
Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft Athanasios Vlontzos Gabriel Sutherland Siddha Ganju Frank Soboczenski 16 2 0 10 Jun 2021
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation Liyuan Xu Heishiro Kanagawa Arthur Gretton CML 18 35 0 07 Jun 2021
On Inductive Biases for Heterogeneous Treatment Effect Estimation Alicia Curth M. Schaar CML 12 77 0 07 Jun 2021
Causal Influence Detection for Improving Efficiency in Reinforcement Learning Maximilian Seitzer Bernhard Schölkopf Georg Martius CML 18 75 0 07 Jun 2021
A Meta Learning Approach to Discerning Causal Graph Structure Justin Wong Dominik Damjakob CML 16 2 0 06 Jun 2021
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data Zhixuan Chu S. Rathbun Sheng Li CML 6 46 0 05 Jun 2021
Causal Effect Inference for Structured Treatments Jean Kaddour Yuchen Zhu Qi Liu Matt J. Kusner Ricardo M. A. Silva CML 177 50 0 03 Jun 2021
Adaptive Multi-Source Causal Inference Thanh Vinh Vo Pengfei Wei T. Hoang Tze-Yun Leong 15 1 0 31 May 2021
Federated Estimation of Causal Effects from Observational Data Thanh Vinh Vo T. Hoang Young Lee Tze-Yun Leong FedML CML 23 13 0 31 May 2021
Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US Jing Ma Yushun Dong Zhengxiong Huang D. Mietchen Jundong Li CML 14 29 0 29 May 2021
Dependent Multi-Task Learning with Causal Intervention for Image Captioning Wenqing Chen Jidong Tian Caoyun Fan Hao He Yaohui Jin CML 27 6 0 18 May 2021
Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach Junhao Hua Ling Yan Huan Xu Cheng Yang 6 16 0 18 May 2021
What can the millions of random treatments in nonexperimental data reveal about causes? Andre F. Ribeiro Frank Neffke Ricardo Hausmann CML 28 1 0 03 May 2021
Neural Networks for Learning Counterfactual G-Invariances from Single Environments S Chandra Mouli Bruno Ribeiro OOD 37 11 0 20 Apr 2021
Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis Xiao Liu Da Yin Yansong Feng Yuting Wu Dongyan Zhao CML AILaw 21 35 0 19 Apr 2021
Sequential Deconfounding for Causal Inference with Unobserved Confounders Tobias Hatt Stefan Feuerriegel CML 19 27 0 16 Apr 2021
Towards a Collective Agenda on AI for Earth Science Data Analysis D. Tuia R. Roscher Jan Dirk Wegner Nathan Jacobs Xiaoxiang Zhu Gustau Camps-Valls AI4CE 39 68 0 11 Apr 2021
Robust Orthogonal Machine Learning of Treatment Effects Yiyan Huang Cheuk Hang Leung Qi Wu Xing Yan OOD CML 16 0 0 22 Mar 2021
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments S. Parbhoo Stefan Bauer Patrick Schwab CML BDL 19 16 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 25 68 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 26 2 0 13 Mar 2021
Size-Invariant Graph Representations for Graph Classification Extrapolations Beatrice Bevilacqua Yangze Zhou Bruno Ribeiro OOD 35 108 0 08 Mar 2021
Combining Interventional and Observational Data Using Causal Reductions Maximilian Ilse Patrick Forré Max Welling Joris M. Mooij OOD CML 25 0 0 08 Mar 2021
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery M. Vowels Necati Cihan Camgöz Richard Bowden CML 37 296 0 03 Mar 2021
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference Sam Witty David D. Jensen Vikash K. Mansinghka CML 13 3 0 23 Feb 2021
On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty Joost R. van Amersfoort Lewis Smith Andrew Jesson Oscar Key Y. Gal UQCV 24 102 0 22 Feb 2021
Causal Mediation Analysis with Hidden Confounders Lu Cheng Ruocheng Guo Huan Liu CML 29 15 0 21 Feb 2021
Training a Resilient Q-Network against Observational Interference Chao-Han Huck Yang I-Te Danny Hung Ouyang Yi Pin-Yu Chen OOD 26 14 0 18 Feb 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression Junhyung Park Uri Shalit Bernhard Schölkopf Krikamol Muandet CML 21 31 0 16 Feb 2021
A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Models Severi Rissanen Pekka Marttinen CML 25 26 0 12 Feb 2021
Do-calculus enables estimation of causal effects in partially observed biomolecular pathways Sara Mohammad-Taheri Jeremy Zucker C. Hoyt Karen Sachs Vartika Tewari Robert Osazuwa Ness and Olga Vitek 33 0 0 12 Feb 2021
Causal Inference for Time series Analysis: Problems, Methods and Evaluation Raha Moraffah Paras Sheth Mansooreh Karami Anchit Bhattacharya Qianru Wang Anique Tahir A. Raglin Huan Liu CML AI4TS 77 104 0 11 Feb 2021
Hierarchical Variational Autoencoder for Visual Counterfactuals Nicolas Vercheval A. Pižurica CML DRL BDL 31 2 0 01 Feb 2021
Learning Matching Representations for Individualized Organ Transplantation Allocation Can Xu Ahmed Alaa Ioana Bica B. Ershoff M. Cannesson M. Schaar OOD 14 7 0 28 Jan 2021
Estimating Average Treatment Effects via Orthogonal Regularization Tobias Hatt Stefan Feuerriegel CML 156 35 0 21 Jan 2021
Intact-VAE: Estimating Treatment Effects under Unobserved Confounding Pengzhou (Abel) Wu Kenji Fukumizu CML 16 13 0 17 Jan 2021
Learning to Ignore: Fair and Task Independent Representations Linda Helen Boedi H. Grabner FaML OOD 13 1 0 11 Jan 2021
Sage: Using Unsupervised Learning for Scalable Performance Debugging in Microservices Yu Gan Mingyu Liang Sundar Dev David Lo Christina Delimitrou 23 4 0 01 Jan 2021
Causal World Models by Unsupervised Deconfounding of Physical Dynamics Minne Li Girish A. Koushik Furui Liu Xu Chen Zhitang Chen Jun Wang SyDa CML 25 12 0 28 Dec 2020
Treatment Targeting by AUUC Maximization with Generalization Guarantees Artem Betlei Eustache Diemert Massih-Reza Amini CML 17 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 14 8 0 17 Dec 2020
RealCause: Realistic Causal Inference Benchmarking Brady Neal Chin-Wei Huang Sunand Raghupathi CML ELM 17 31 0 30 Nov 2020