Let's say I have some mechanism that theoretically says $a_t$ positively affects $b_t$ and $b_t$ positively affects $c_t$. I have data on $a_t$ and $c_t$ but not $b_t$. Thus, $b_t$ is unobserved. I run a regression: $c_t$ = $\alpha_0 + \alpha_1 a_t$ where $\alpha_1 > 0$. Can I say something about the empirical effect of $a_t$ on $b_t$ although $b_t$ is unobserved? In this example, may be I can say that if $a_t$ positively affects $c_t$, then more likely it positively affects $b_t$? Thus, I am speculating about the effect of $a_t$ on $b_t$ here given the theory and the regression model.
Lemme also ask a related question. If $a_t$ affects $b_t$ and $b_t$ affects $c_t$, I can not run the regression $c_t$ = $\alpha_0 + \alpha_1 a_t + \alpha_2 b_t$ since $a_t$ and $b_t$ would be collinear, right? And I can only do that in a multiple equation model like the SVAR?