I am dealing with logistic regression, trying to identify variables which have a causal relationship with a binary response. The way I usually do it is to try variables one by one and visualize the probability of positive outcome curve, and check if it is flat or has a good curve. If it is the latter, then it means there's a causal relationship.
I wonder if there is a better routine? Especially if I have a huge number of variables to check, while some of them are not the ones that have a relationship with the observations. What would be the cons if I throw too many variables into logistic regression?