Alright, in the study I referenced in post #6, the authors play around with the affects of the following types of variables in the use of propensity scores:

-variables associated with exposure and outcome (confounder),

-variables associated solely with exposure, and

-variables associated solely with outcome

The purpose of the study was to examine the effects of including these types of variables in the propensity scores of models and estimating exposure effects versus the true exposure effect per simulated dataset.

In the below equation taken from the article:

alpha-subscript 4 is the true exposure effect on Y, derived from the simulation (e.g., 0.5); and

gamma-hat is the estimated exposure effect on Y, which is a log relative risk in this study.

Can some one help me better understand their approach of determining the squared difference of a log relative risk - true effect. I get why they did everything that they did, I am just getting confused by how they can get an MSE out of a log relative risk, pretty much since it is not a continuous parameter. I guess you can do this, so you can just find differences in effects and create an MSE whenever you want?