# Causality Help!?!?

I have a regression model with 1 Y variable and 2 X variables (R sq = .66). The Y variable, and one of the X variables are likely interdependent (based on deep domain knowledge of 20+ years).

When I swap the Y variable and the X variable (making the Y independent and X dependent )…. the resulting new regression model shows the new X variable (formerly Y) with a value of .49. This is pretty strong, but significantly weaker than the former X variable (now Y variable) which previously had a p value of .10.

What conclusions can I make? Can I say that the original X variable has ~twice as much effect on the trend (.90 / .51) as the new (swapped) X variable?  Even better, can I say that ~2/3rds [.90/(.90+.51)] of trend is a function of the original X variable (this is actually what I believe based on domain knowledge)?

(Note - I know only enough to be dangerous about regression)

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