Questions about causal analysis class in econML

I have some questions about the causal analysis class in econML.

1. Does anyone know how to overcome this issue when I fit the model with the training data. I don't know where I can increase the number of iterations in this class:

Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.004085702084360321, tolerance: 0.0025136819966382977

2. Does the causal analysis compute the causal effect of each covariate that has been passed to feature_inds one by one, meaning that the first covariate on that list will be used as treatment first and then rest will be controls and repeat the same process for every covariate that has been passed into feature_inds? How does that work?
3. How can you change the hypermeter that the model get fine-tuned on so that it can include wider range?
4. What is the difference if I don't include certain features into feature_inds, will they still be used in the model?
5. How is heterogeneity_inds different than feature_inds in the model? What if I include some of the heterogeneity_inds into feature_inds ?
6. I know that the class uses causalforestDML at some points, is it ok to includes features in the data that is not controls between treatments and outcomes? Also, will it be problematic to include variables that are highly correlated?
7. How does the model handle the potential interaction effects of the features?

The issue names the causal analysis class and CausalForestDML; start by reading their existing API documentation and the definitions of feature_inds and heterogeneity_inds. A complete response would address the iteration setting, tuning range, covariate use, index overlap, correlated variables, and interaction handling.