# Propensity score matching: covariate balance

I have one concern about propensity score matching's assumption. It seems that what propensity score is doing is to say that the choice of treatment depends on pre-treatment covariates.

Suppose I am to model the effect of networking on grant proposal (binary outcome) for individual researcher, where networking ($$networking$$) is measured by a dichotomous variable - reputation of past coauthors ($$Z$$) - high (1) or low (0). There are covariates such as researchers' own reputation ($$rep$$) and gender ($$gen$$).

My question then is: given the underlying assumption that those covariate values are pre-treatment, $$rep$$ would be a variable that actually changes along with $$Z$$, which means it is NOT pre-treatment but measured at the same time as the treatment $$Z$$, can I still calculate the propensity score $$P(Z=1|gen,rep)$$?

I found this paper: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1541-0420.2005.00356.x but I feel like it is not the answer to my question.

Any pointers or explanations are greatly appreciated. Thank you!

• Thanks for your reply but I feel like your understanding of the problem is not what I meant to say. My emphasis is not on $Y$ (outcome) but more on $Z$ (treatment) and $rep$ (one of the control, candidate reputation). Specifically, my point is $rep$ is not measured after the grant decision but instead, measured at the same time as $Z$ (the treatment). In other words, the covariates are not pre-treatment but at-the-same-time-as-treatment. How could this affect PSM? Thanks! – Zhiya Dec 29 '18 at 5:12