Aegis-GPU5.4 Relationships, trends and confounders
Correlation versus cause, site-specific confounders and spotting trends.
Key points
Correlation means two variables move together. It is not proof of cause. A hidden third factor, a confounder, can drive both.
What NVIDIA says (2)
“For any use case, be aware of the variables that are highly correlated, as they could skew results.”
“Take this into account if you are using any future algorithm that assumes the variables are independent, such as linear regression .”
A confounding factor affects both inputs and outcomes, making a false link look real. Single-site data can carry such site-specific bias.
What NVIDIA says (1)
“Medical institutions have had to rely on their own data sources, which can be biased by, for example, patient demographics, the instruments used or clinical specializations.”
Linking charts lets you slice data quickly and spot trends over time.
What NVIDIA says (1)
“a clear pattern emerges between weekday and weekend trips”
Key terms
- Cross-filtering: Linked charts where selecting data in one filters all the others.
- Correlation: A measure of how two variables move together; it does not prove cause.
- Confounding factor: A hidden variable that affects both inputs and outcomes and can create a false link.
Sample question
A correlation matrix shows two features are strongly correlated. Why does that matter?
Show the answer
Answer: Highly correlated variables can skew results, especially for models that assume independence such as linear regression
Correlation means two variables move together. It is not proof of cause. A hidden third factor, a confounder, can drive both.
What NVIDIA says (2)
“For any use case, be aware of the variables that are highly correlated, as they could skew results.”
“Take this into account if you are using any future algorithm that assumes the variables are independent, such as linear regression .”
Practice 5.4 (3 questions) Full Data Analysis and Visualization guide