Using Embeddings to Correct for Unobserved Confounding in Networks

Using Embeddings to Correct for Unobserved Confounding in Networks

11 February 2019

Papers citing "Using Embeddings to Correct for Unobserved Confounding in Networks"

12 / 12 papers shown

Title
Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning Weilin Chen Ruichu Cai Zeqin Yang Jie Qiao Yuguang Yan Zijian Li Zhifeng Hao CML 44 7 0 06 May 2024
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling Dingyuan Zhu Daixin Wang Qing Cui Kun Kuang Yan Zhang Yulin Kang Jun Zhou 40 3 0 11 Mar 2024
Learning Individual Treatment Effects under Heterogeneous Interference in Networks Ziyu Zhao Yuqi Bai Kun Kuang Ruoxuan Xiong Fei Wu CML 24 6 0 25 Oct 2022
Estimating Social Influence from Observational Data Dhanya Sridhar Caterina De Bacco David M. Blei 32 3 0 24 Mar 2022
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies Shachi Deshpande Kaiwen Wang Dhruv Sreenivas Zheng Li Volodymyr Kuleshov CML SyDa 16 11 0 18 Mar 2022
Heterogeneous Peer Effects in the Linear Threshold Model Christopher Tran Elena Zheleva 22 10 0 27 Jan 2022
$β$ -Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap Pengzhou (Abel) Wu Kenji Fukumizu CML 35 14 0 11 Oct 2021
Towards Principled Causal Effect Estimation by Deep Identifiable Models Pengzhou (Abel) Wu Kenji Fukumizu BDL OOD CML 30 3 0 30 Sep 2021
Desiderata for Representation Learning: A Causal Perspective Yixin Wang Michael I. Jordan CML 32 82 0 08 Sep 2021
GraphITE: Estimating Individual Effects of Graph-structured Treatments Shonosuke Harada H. Kashima CML 24 21 0 29 Sep 2020
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data Ruocheng Guo Jundong Li Huan Liu CML OffRL 33 21 0 22 Dec 2019
Adapting Text Embeddings for Causal Inference Victor Veitch Dhanya Sridhar David M. Blei CML 17 21 0 29 May 2019