I am a bit stuck here. I am analyzing some data to see if there are heterogeneous treatment effects. To do that I am essentially running the three models below (and an Linear probability model (LPM)). My dependent variable is binary and I run logit models.
When I run two separate models (column 1 & 2) I find that for the group with the characteristic the treatment effect is significant not for the group without the characteristic. This leads me to think that there is a heterogeneous treatment effect. When I then run an overall model including the interaction term (column 3) this interaction is not significant.
I tried to understand what is going on here and searched different sources for explanations. Most sources pointed out that this is the case if there is collinearity issue with some other covariates in the model and that the characteristic needs to be interacted with all covariates in the full model. This cannot be a problem in my case as I have no other covariates in the model. My only independent variables are the characteristic and the treatment.
Can somebody explain to me when such a case can happen?