When estimating causal effects, you want to compare individuals as similar as possible. It is from this need that stems the exchangeability (/ignorability) or conditional exchangeability (/ conditional ignorability) assumption, stating that the probability of treatment must be "as good as random".
Another assumption need is $0<P(t_i = t| x)<1$, $x$ being a vector of independent variable.
My question is, when doing policy evaluation, the assignment-to-treatment mechanism is deterministic rather than stochastic. Living in a certain state in a certain year determines you being treated or not, with probability 1. How are those assumptions compatible with this?
My only current precaution is to make the residents of treated and control states comparable by conditioning on a number of relevant variables, but the doubt about what just asked remains.