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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 confounding

24 July 2020
Justin Grimmer
D. Knox
Brandon M Stewart
    CML
ArXivPDFHTML

Papers citing "Naïve regression requires weaker assumptions than factor models to adjust for multiple cause confounding"

4 / 4 papers shown
Title
Substitute adjustment via recovery of latent variables
Substitute adjustment via recovery of latent variables
Jeffrey Adams
Niels Richard Hansen
CML
34
1
0
01 Mar 2024
Debiasing Recommendation by Learning Identifiable Latent Confounders
Debiasing Recommendation by Learning Identifiable Latent Confounders
Qing Zhang
Xiaoying Zhang
Yang Liu
Hongning Wang
Min Gao
Jiheng Zhang
Ruocheng Guo
CML
51
10
0
10 Feb 2023
Sample Observed Effects: Enumeration, Randomization and Generalization
Sample Observed Effects: Enumeration, Randomization and Generalization
Andre F. Ribeiro
CML
21
4
0
09 Aug 2021
Learning latent causal graphs via mixture oracles
Learning latent causal graphs via mixture oracles
Bohdan Kivva
Goutham Rajendran
Pradeep Ravikumar
Bryon Aragam
CML
33
47
0
29 Jun 2021
1