# Can I use PCA to study how variables affect each other?

I'd like to know what PCA tells me about how the variables affect each other.

For example, let's say I've three variables Cholesterol, Exercise, Calorie Intake and Sleep. I want to know how Exercise, Sleep and Calorie Intake affect Cholesterol. Will the Cholesterol be lower (or higher) if I eat more calories or if I exercise more? After PCA I get the following:

Standard deviations:
[1] 2.0562689 0.4926162 0.2796596 0.1543862

Rotation:
PC1         PC2         PC3        PC4
Cholesterol  0.36138659 -0.65658877  0.58202985  0.3154872
Exercise    -0.08452251 -0.73016143 -0.59791083 -0.3197231
CalorieI     0.85667061  0.17337266 -0.07623608 -0.4798390
Sleep        0.35828920  0.07548102 -0.54583143  0.7536574

PC1     PC2    PC3     PC4
Standard deviation     2.0563 0.49262 0.2797 0.15439
Proportion of Variance 0.9246 0.05307 0.0171 0.00521
Cumulative Proportion  0.9246 0.97769 0.9948 1.00000


What I understand from this is how each individual component account for the variance in the data. The only thing I can say here is that PC1 and PC2 has a cumulative variance which account for ~98% which I implicitly interpret as PC3 and PC4 having no affect on the data at all. I understand that PC1 and PC2 are enough to explain all the data from the four given variables but does it say anything about how the variables affect each other?

• I think Cholesterol be higher if you eat more calories through Low density lipoproteins. But PCA is not a tool to investigate how variables effect or affect each other. Regression techniques would be more appropriate for that. Aug 12, 2015 at 15:44
• PCA says absolutely nothing about how variables affect each other. It can only describe how they are mutually related. See stats.stackexchange.com/questions/534.
– whuber
Aug 12, 2015 at 15:57
• Yes, you're probably right, these are just values I made up it so that I could make my question more precise. Thanks for your answer Aug 12, 2015 at 15:57
• My comment is not based on any analysis of your data. It doesn't matter what values your data have--you cannot use PCA (by itself) to use data like this to conclude anything about how changing calories or exercise will affect cholesterol.
– whuber
Aug 12, 2015 at 15:59
• @Antoni Use the language of association, relationship, variation, and correlation rather than of causes, effects, dependencies, responses, or explanations. The former describe patterns in the data--which it is legitimate to interpret--while the latter rely on information that is not in the data.
– whuber
Aug 12, 2015 at 16:05