Best practices for selecting categorical features I'm trying to create a classifier that will predict whether someone will attend an interview or not. Each data point is for a single candidate and contains details such as the location of the interview, the candidate's current location, the job skill requirements, interview type etc. All of the data is categorical.

There are also some features, which you may not think would have an impact on the candidate's attendance e.g. marital status. My initial thought was, based on "common sense" (I use this term loosely), to drop this feature, but I wanted to take a more rigorous approach to determine the importance of this. However, I am unsure of what is best practice. How do I go about determining whether this is a feature that can be removed or not?

Secondly, is there a downside to keeping it even if it has limited predictive power? Aside from creating a more cumbersome model, could it adversely impact the accuracy of a prediction? 

Thanks.
For removing categorical variable, generally used techniques is chi-square distribution based selection. The technique falls under general umbrella term univariate feature selection.