# 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)

## Answer

**Answers can be viewed only if**

- The questioner was satisfied and accepted the answer, or
- The answer was disputed, but the judge evaluated it as 100% correct.

- answered
- 156 views
- $10.00

### Related Questions

- "Estimate of standard error of the proportion"
- Questions for Statistics Project
- Statistics tasks
- Finding the probability that a roughly normal distributed will have the highest value among multiple curves
- Operations research
- Normal distribution & Probability
- Bivariate Normality questions
- Mathematical modeling