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Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders

1 February 2019

Papers citing "Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders"

36 / 36 papers shown

Title
When Counterfactual Reasoning Fails: Chaos and Real-World Complexity Yahya Aalaila Gerrit Großmann Sumantrak Mukherjee Jonas Wahl Sebastian Vollmer CML LRM 150 0 0 31 Mar 2025
DeCaFlow: A Deconfounding Causal Generative Model Alejandro Almodóvar Adrián Javaloy J. Parras Santiago Zazo Isabel Valera CML 82 0 0 19 Mar 2025
Defining Expertise: Applications to Treatment Effect Estimation Alihan Huyuk Qiyao Wei Alicia Curth M. Schaar CML 82 2 0 01 Mar 2024
Right on Time: Revising Time Series Models by Constraining their Explanations Maurice Kraus David Steinmann Antonia Wüst Andre Kokozinski Kristian Kersting AI4TS 94 4 0 20 Feb 2024
Clairvoyance: A Pipeline Toolkit for Medical Time Series Daniel Jarrett Chang Jo Kim Ioana Bica Zhaozhi Qian A. Ercole M. Schaar AI4TS 89 36 0 28 Oct 2023
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data Mouad El Bouchattaoui Myriam Tami Benoit Lepetit P. Cournède CML OOD 219 2 0 16 Oct 2023
CTP:A Causal Interpretable Model for Non-Communicable Disease Progression Prediction Zhoujian Sun Wenzhuo Zhang Zhengxing Huang Nai Ding Cheng Luo CML 97 2 0 18 Aug 2023
Deep Causal Learning for Robotic Intelligence Yongqian Li CML 82 5 0 23 Dec 2022
On How AI Needs to Change to Advance the Science of Drug Discovery Kieran Didi Matej Zečević CML 66 1 0 23 Dec 2022
Deep Causal Learning: Representation, Discovery and Inference Zizhen Deng Xiaolong Zheng Hu Tian D. Zeng CML BDL 125 11 0 07 Nov 2022
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation Ioana Bica M. Schaar OOD CML 72 22 0 08 Oct 2022
Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions V. T. Trifunov M. Shadaydeh Joachim Denzler CML BDL 73 3 0 23 Sep 2022
Estimating individual treatment effects under unobserved confounding using binary instruments Dennis Frauen Stefan Feuerriegel CML 91 20 0 17 Aug 2022
Variational Temporal Deconfounder for Individualized Treatment Effect Estimation from Longitudinal Observational Data Zheng Feng M. Prosperi Jiang Bian CML 52 0 0 23 Jul 2022
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations Nabeel Seedat F. Imrie Alexis Bellot Zhaozhi Qian M. Schaar OOD CML 57 52 0 16 Jun 2022
Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes Changchang Yin Ruoqi Liu Jeffrey Caterino Ping Zhang OffRL 102 5 0 19 May 2022
Estimating average causal effects from patient trajectories Dennis Frauen Tobias Hatt Valentyn Melnychuk Stefan Feuerriegel OOD CML 95 25 0 02 Mar 2022
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects Tobias Hatt Jeroen Berrevoets Alicia Curth Stefan Feuerriegel M. Schaar CML 92 32 0 25 Feb 2022
Causal Knowledge Guided Societal Event Forecasting Songgaojun Deng Huzefa Rangwala Yue Ning AI4TS 61 2 0 10 Dec 2021
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies Milan Kuzmanovic Tobias Hatt Stefan Feuerriegel CML 94 22 0 06 Dec 2021
A Taxonomy for Inference in Causal Model Families Matej Zevcević Devendra Singh Dhami Kristian Kersting 89 1 0 22 Oct 2021
Relating Graph Neural Networks to Structural Causal Models Matej Zečević Devendra Singh Dhami Petar Velickovic Kristian Kersting AI4CE CML 134 56 0 09 Sep 2021
DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction Siyuan Ren Bin Guo LongBing Cao Ke Li Jiaqi Liu Zhiwen Yu 52 8 0 18 Aug 2021
Adaptive Multi-Source Causal Inference Thanh Vinh Vo Pengfei Wei T. Hoang Tze-Yun Leong 103 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 80 13 0 31 May 2021
Sequential Deconfounding for Causal Inference with Unobserved Confounders Tobias Hatt Stefan Feuerriegel CML 97 29 0 16 Apr 2021
NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments S. Parbhoo Stefan Bauer Patrick Schwab CML BDL 72 16 0 20 Mar 2021
Towards Causal Representation Learning Bernhard Schölkopf Francesco Locatello Stefan Bauer Nan Rosemary Ke Nal Kalchbrenner Anirudh Goyal Yoshua Bengio OOD CML AI4CE 166 323 0 22 Feb 2021
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models Matej Zečević Devendra Singh Dhami Athresh Karanam S. Natarajan Kristian Kersting CML TPM 117 33 0 20 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 113 111 0 11 Feb 2021
Demand Forecasting of Individual Probability Density Functions with Machine Learning Felix Wick U. Kerzel Martin Hahn Moritz Wolf Trapti Singhal Daniel Stemmer Jakob Ernst M. Feindt OOD AI4TS 93 4 0 15 Sep 2020
Estimating Individual Treatment Effects with Time-Varying Confounders Ruoqi Liu Changchang Yin Ping Zhang CML 95 27 0 27 Aug 2020
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks Ioana Bica James Jordon M. Schaar CML 101 106 0 27 Feb 2020
A Survey on Causal Inference Liuyi Yao Zhixuan Chu Sheng Li Yaliang Li Jing Gao Aidong Zhang CML 131 518 0 05 Feb 2020
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 148 1 0 03 Apr 2019
Weighted Tensor Completion for Time-Series Causal Inference Debmalya Mandal David C. Parkes 41 2 0 12 Feb 2019