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Towards Clarifying the Theory of the Deconfounder

Towards Clarifying the Theory of the Deconfounder

10 March 2020
Yixin Wang
David M. Blei
    CMLAI4CE
ArXiv (abs)PDFHTML

Papers citing "Towards Clarifying the Theory of the Deconfounder"

11 / 11 papers shown
Substitute adjustment via recovery of latent variables
Substitute adjustment via recovery of latent variables
Jeffrey Adams
Niels Richard Hansen
CML
229
2
0
01 Mar 2024
A Counterfactual Fair Model for Longitudinal Electronic Health Records
  via Deconfounder
A Counterfactual Fair Model for Longitudinal Electronic Health Records via DeconfounderIndustrial Conference on Data Mining (IDM), 2023
Zheng Liu
Xiaohan Li
Philip Yu
CML
327
3
0
22 Aug 2023
Estimating Treatment Effects from Irregular Time Series Observations
  with Hidden Confounders
Estimating Treatment Effects from Irregular Time Series Observations with Hidden ConfoundersAAAI Conference on Artificial Intelligence (AAAI), 2023
Defu Cao
James Enouen
Yujing Wang
Xiangchen Song
Chuizheng Meng
Hao Niu
Yan Liu
CML
163
26
0
04 Mar 2023
Mitigating Frequency Bias in Next-Basket Recommendation via
  Deconfounders
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders
Xiaohan Li
Zheng Liu
Luyi Ma
Gabriele Tolomei
Stephen D. Guo
Philip Yu
Kannan Achan
CML
178
8
0
16 Nov 2022
Mitigating Health Disparities in EHR via Deconfounder
Mitigating Health Disparities in EHR via DeconfounderACM International Conference on Bioinformatics, Computational Biology and Biomedicine (ACM-BCB), 2022
Zheng Liu
Xiaohan Li
Philip Yu
CML
166
11
0
28 Oct 2022
Estimating Social Influence from Observational Data
Estimating Social Influence from Observational DataCLEaR (CLEaR), 2022
Dhanya Sridhar
Caterina De Bacco
David M. Blei
211
5
0
24 Mar 2022
Deep Causal Reasoning for Recommendations
Deep Causal Reasoning for RecommendationsACM Transactions on Intelligent Systems and Technology (ACM TIST), 2022
Yaochen Zhu
Jing Yi
Jiayi Xie
Zhenzhong Chen
CMLBDL
248
17
0
06 Jan 2022
Desiderata for Representation Learning: A Causal Perspective
Desiderata for Representation Learning: A Causal Perspective
Yixin Wang
Sai Li
CML
237
89
0
08 Sep 2021
Sequential Deconfounding for Causal Inference with Unobserved
  Confounders
Sequential Deconfounding for Causal Inference with Unobserved ConfoundersCLEaR (CLEaR), 2021
Tobias Hatt
Stefan Feuerriegel
CML
325
30
0
16 Apr 2021
Naïve regression requires weaker assumptions than factor models to
  adjust for multiple cause confounding
Naïve regression requires weaker assumptions than factor models to adjust for multiple cause confoundingJournal of machine learning research (JMLR), 2020
Justin Grimmer
D. Knox
Brandon M Stewart
CML
214
13
0
24 Jul 2020
Counterexamples to "The Blessings of Multiple Causes" by Wang and Blei
Counterexamples to "The Blessings of Multiple Causes" by Wang and Blei
Elizabeth L. Ogburn
I. Shpitser
E. T. Tchetgen
CMLAI4CE
167
15
0
17 Jan 2020
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