vietjtnguyen

vietjtnguyen

vietjtnguyen

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vietjtnguyen

:loop
powershell -Command "&{ svn status | ? { $_ -match '^!\s+(.*)' } | %% { svn rm $Matches[1] }  }"
svn add --force * --auto-props --parents --depth infinity
svn commit -m "Autosave %date% %time%"
svn update
TIMEOUT /t 60
goto loop

vietjtnguyen

Causation implies correlation but not necessarily the other way around. Correlation can be a result of confounding variables (X and Y are correlated because some latent variable Z is causing both X and Y).

I think you'd enjoy a read of Judea Pearl's "Causality". If you could get your hands on the book and read just the first chapter that should suffice.

I'll attempt a sweeping blurb but I can't guarantee its correctness. I'm probably going to screw up some statistical concepts. Also, I'm going to ignore sampling effects (which can cause spurious correlations and stuff).

---

Anyway, say you're observing a system. It has two variables: X and Y. You collect observational data and fit a statistical model to your data. Say we find a correlation (dependence) between X and Y in the data.

vietjtnguyen

vietjtnguyen

vietjtnguyen

vietjtnguyen

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