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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

Journal of machine learning research (JMLR), 2020
24 July 2020
Justin Grimmer
D. Knox
Brandon M Stewart
    CML
ArXiv (abs)PDFHTML

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

3 / 3 papers shown
Debiasing Recommendation by Learning Identifiable Latent Confounders
Debiasing Recommendation by Learning Identifiable Latent ConfoundersKnowledge Discovery and Data Mining (KDD), 2023
Qing Zhang
Xiaoying Zhang
Yang Liu
Hongning Wang
Min Gao
Jiheng Zhang
Ruocheng Guo
CML
361
19
0
10 Feb 2023
Sample Observed Effects: Enumeration, Randomization and Generalization
Sample Observed Effects: Enumeration, Randomization and GeneralizationScientific Reports (Sci Rep), 2021
Andre F. Ribeiro
CML
281
3
0
09 Aug 2021
Learning latent causal graphs via mixture oracles
Learning latent causal graphs via mixture oraclesNeural Information Processing Systems (NeurIPS), 2021
Bohdan Kivva
Goutham Rajendran
Pradeep Ravikumar
Bryon Aragam
CML
460
59
0
29 Jun 2021
1
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