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Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data

30 April 2019

Xin Du

Lei Sun

W. Duivesteijn

Alexander G. Nikolaev

Papers citing "Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data"

28 / 28 papers shown

Title
Orthogonal Representation Learning for Estimating Causal Quantities Valentyn Melnychuk Dennis Frauen Jonas Schweisthal Stefan Feuerriegel CML OOD BDL 136 2 0 06 Feb 2025
Defining Expertise: Applications to Treatment Effect Estimation Alihan Huyuk Qiyao Wei Alicia Curth M. Schaar CML 68 2 0 01 Mar 2024
Neural Causal Abstractions K. Xia Elias Bareinboim CML NAI 103 7 0 05 Jan 2024
Adversarially Balanced Representation for Continuous Treatment Effect Estimation Amirreza Kazemi Martin Ester CML OOD 91 3 0 17 Dec 2023
Adversarial Distribution Balancing for Counterfactual Reasoning Stefan Schrod Fabian H. Sinz Michael Altenbuchinger OOD CML 88 1 0 28 Nov 2023
Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation Valentyn Melnychuk Dennis Frauen Stefan Feuerriegel CML 79 10 0 19 Nov 2023
Towards Causal Foundation Model: on Duality between Causal Inference and Attention Jiaqi Zhang Joel Jennings Agrin Hilmkil Nick Pawlowski Cheng Zhang Chao Ma CML 112 14 0 01 Oct 2023
Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework? Yonghe Zhao Q. Huang Shuai Fu Huashan Sun CML 32 0 0 22 Aug 2023
VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference Yonghe Zhao Q. Huang Siwei Wu Yun Peng Huashan Sun BDL CML 32 0 0 02 Aug 2023
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network Yonghe Zhao Q. Huang Haolong Zeng Yun-Wen Pen Huashan Sun CML OOD BDL 38 2 0 24 Jul 2023
Advancing Counterfactual Inference through Nonlinear Quantile Regression Shaoan Xie Erdun Gao Bin Gu Tongliang Liu Kun Zhang 92 1 0 09 Jun 2023
Integrating Earth Observation Data into Causal Inference: Challenges and Opportunities Connor Jerzak Fredrik D. Johansson Adel Daoud CML 72 12 0 30 Jan 2023
Deep Causal Learning for Robotic Intelligence Yongqian Li CML 73 5 0 23 Dec 2022
Deep Causal Learning: Representation, Discovery and Inference Zizhen Deng Xiaolong Zheng Hu Tian D. Zeng CML BDL 119 11 0 07 Nov 2022
Neural Causal Models for Counterfactual Identification and Estimation K. Xia Yushu Pan Elias Bareinboim CML 100 39 0 30 Sep 2022
A Survey of Deep Causal Models and Their Industrial Applications Zongyu Li Xiaoning Guo Siwei Qiang CML AI4CE 76 8 0 19 Sep 2022
Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects Guanglin Zhou Lina Yao Xiwei Xu Chen Wang Liming Zhu OOD CML BDL 57 2 0 13 Aug 2022
Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa Connor Jerzak Fredrik D. Johansson Adel Daoud CML 76 6 0 13 Jun 2022
Diffusion Causal Models for Counterfactual Estimation Pedro Sanchez Sotirios A. Tsaftaris DiffM BDL 99 76 0 21 Feb 2022
Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation Yi-Fan Zhang Hanlin Zhang Zachary Chase Lipton Li Erran Li Eric P. Xing OODD 115 30 0 02 Feb 2022
Cycle-Balanced Representation Learning For Counterfactual Inference Guanglin Zhou L. Yao Xiwei Xu Chen Wang Liming Zhu CML OOD 38 12 0 29 Oct 2021
Estimating Potential Outcome Distributions with Collaborating Causal Networks Tianhui Zhou William E Carson IV David Carlson CML 208 8 0 04 Oct 2021
The Causal-Neural Connection: Expressiveness, Learnability, and Inference K. Xia Kai-Zhan Lee Yoshua Bengio Elias Bareinboim CML 81 111 0 02 Jul 2021
Robust Orthogonal Machine Learning of Treatment Effects Yiyan Huang Cheuk Hang Leung Qi Wu Xing Yan OOD CML 57 0 0 22 Mar 2021
Balance Regularized Neural Network Models for Causal Effect Estimation Mehrdad Farajtabar Andrew Lee Yuanjian Feng Vishal Gupta Peter Dolan Harish Chandran M. Szummer CML 46 6 0 23 Nov 2020
Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models Yuta Saito Shota Yasui OOD CML 26 8 0 11 Sep 2019
Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck Sungyub Kim Yong-Ho Baek Sung Ju Hwang Eunho Yang CML 23 1 0 07 Jun 2019
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition Vincent Dorie J. Hill Uri Shalit M. Scott D. Cervone CML 263 288 0 09 Jul 2017