I am looking for a direct effect and I have a total of 12 variables (exposure, outcome, 8 observed, 2 possibly important unobserved variables).
Dagitty packages gives 5 adjustment sets possible for adjusting. The goal of regression is to prove the difference between exposure variable levels in outcome variable.
What approaches can be used or should be considered for choosing the best adjustment set for matching? More the better?
Also, am I correct that by temporarily turning unobserved variables as observed ones, I can check their role/importance in terms of adjusting? E.g. if unobserved variables are recommended for adjusting, then they would be important in our work (I must admit a limitation). However, if dagitty package gives such options for analysing a direct effect.
- observedA, observedB, observedC
- observedA, observedD, unobservedU
As both sets are recommended, can I just use the first and say that I can bypass the need for unobserved variables for analysis? Or in other words, My adjusting would be good enough without unobserved variables? Of course, we must assume that the DAG is correct.