Can we know which variables are certain Principal components are made of in PCA? In PCA, if we have ax1+bx2+c*x3 = y, say we get 2 principal components(d1,d2) out of x1,x2,x3. Can we know which of the x1,x2,x3 and in how much magnitude were used to create the d1 and d2?
If not, how is PCA useful for determining which variables affect your outcome when all you get is a mishmash of original variables?
 A: A common method for understanding how the PCs are related to the original variables is to calculate the correlation between the variables and the PC. It is important to use correlations rather than the coefficients that define the linear combinations to avoid scaling effects.
It can sometimes happen, however, that certain variables will have low correlations individually, but higher correlations in combinations with other variables. You can use the variable pairs heat map to understand not only how individual variables correlate with a PC, but also how sums and differences of standardized variables are correlated with a PC. In the graph shown below, the diagonal shows absolute correlations
between the individual variables and the PC; the upper
triangle shows absolute correlations between the differences
$Z_i − Z_j$ and the PC; and the lower triangle shows absolute correlations
between the sums $Z_i + Z_j$ and the PC.

Further details are given in Teaching Principal Components Using Correlations, Multivariate Behavioral Research
Volume 52, 2017 - Issue 5.
