Implement "Additive Counterfactually Fair" estimator

[cosmicBboy]

The main idea is to:

• train linear models using some linear estimator M to predict each feature x_i using
  the protected class attribute s as input.
• then compute the residuals epsilon_ij between the predicted feature values
  and true feature values for each observation i for each feature j.
• The final model is then trained using epsilon_ij as input features to predict the target y.

[cosmicBboy]

For residuals on binary input variables, we have to use either deviance of pearson residuals.
See these resources for implementation details:

• https://web.as.uky.edu/statistics/users/pbreheny/760/S11/notes/4-12.pdf
• https://www.stat.ubc.ca/~rollin/teach/536w10/lec16.pdf
• http://data.princeton.edu/wws509/notes/c3s8.html