Summary of "COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing"
- The COVID-19 testings tend to focus on already hospitalized people, not accounting for the people with mild symptoms and the asymptomatics leading to selection bias.
- The differences in death rates across countries may not be attributed to clinical, demographic, and environmental factors.
- Together with random sampling/testing, the Bayesian Network model allows causal explanations from data for better predictions.
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SARS-CoV-2 (COVID-19)
Biomedical Sciences
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Summary of "COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing"
How do Bayesian Networks work?
Helpful article about Bayesian Networks
Belief Propagation in Bayesian Networks
Conditioning on a Variable
Conditional Probability Table
Bayesian Networks and Independence
Criticism of Bayesian Network from the Judea Pearl
From objectivity to subjectivity
Bayesian Solution for Monty Hall’s Paradox
Summary of "COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing"
Structure Learning
Example: Selection Bias in Case-Control Studies
Another example of selection bias in Case-control Studies
Summary of "COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing"
Non-response Bias
1936 Literary Digest Straw Poll
What type of systematic error occurs when the method used to select participants results in a sample that fails to accurately represent the broader target population?