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Raunak Kaushik
Raunak Kaushik

Raunak Kaushik

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From A Technical Primer On Causality by adam kelleher

So we’ve seen a few results from looking at this graph. X and Z are generally dependent, even though …

From A Technical Primer On Causality by adam kelleher

So we’ve seen a few results from looking at this graph. X and Z are generally dependent, even though there’s no causal relationship between them. This is called “confounding,” and Y is called a “confounder”. We’ve also seen that conditioning on Y causes X and Z to become statistically independent. You can simply measure this as the regression coefficient of X on Z, conditional on Y.

From A Technical Primer On Causality by adam kelleher

so Z is indeed independent of X (given Y). This comes out directly as a result of the (statistical properties of) the causal structure of the …

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A One-Stop Shop for Principal Component Analysis

Matt Brems