Regarding the group formation in an oil-price experiment I'd like to have your opinion on the following; 

Assuming that I'm doing a research on setting the optimal price for gasoline for a company and my client wants me to perform a study on​ customers' gasoline purchasing habits when they notice an increase in the price of gas. 

I have a training data on these categories: 


People who have not noticed a​ gas-price increase
  
People who drive electric cars
  
People who have recently noticed a​ gas-price increase
  
People who ride their bikes to work
  
People who ride the bus


Where I am going to form a treatment and a control group. Though I'm not sure how to pick the categories I'll be including in the test. 

My opinion (definitely not an expert), I would ignore the people who ride their bikes to work and who drive electric cars, as they are simply out of the interest of the research. And randomly distribute the other categories' members. Thing I'm not sure is about the people who ride the bus; should I include them or not? 

However my other opinion is that there might be also some switches for EVs, bikes or public bus. In this case I'd better include every category in the data. 

Thanks in advance for every opinion.
Without knowing more about your specific project, you probably want to look at difference-in-difference methodologies. It's the bread and butter of this sort of psudo-experiment. 

I see you use the phrases "train" and "test".  Be very careful, typically machine learning approaches are designed to get very good $\hat{Y}$s.  Economists in general don't care about $\hat{Y}$s much at all. We care about having good $\hat{\beta}$'s. It will take some time to get used to that idea.

Naive use of traditional ML techniques will lead to highly biased estimators and very misleading estimates of $\hat{Y}$ that often lead to bad policy decisions.