Published on in Vol 8, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31306, first published .
Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation

Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation

Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation

Nathaniel Stockham   1 , MSc ;   Peter Washington   2 , PhD ;   Brianna Chrisman   2 , PhD ;   Kelley Paskov   3 , MSc ;   Jae-Yoon Jung   4 , PhD ;   Dennis Paul Wall   4, 5 , PhD

1 Neurosciences Interdepartmental Program, Stanford University, Palo Alto, CA, United States

2 Department of Bioengineering, Stanford University, Stanford, CA, United States

3 Biomedical Informatics Program, Stanford University, Stanford, CA, United States

4 Department of Biomedical Data Science, Stanford University, Stanford, CA, United States

5 Department of Pediatrics, Stanford University, Stanford, CA, United States

Corresponding Author:

  • Nathaniel Stockham, MSc
  • Neurosciences Interdepartmental Program
  • Stanford University
  • 3145 Porter Dr
  • Palo Alto, CA, 94304
  • United States
  • Phone: 1 2056021832
  • Email: stockham@stanford.edu