Confidence Interval for categorical outcome

Hi @kbattocchi, keith, I am building a CausalForest where i have Treatment Variable which is multi categorical [0,1,2,3,5] and the outcome is [0,1], where 1 being severe.

econml_causalForest = CausalForestDML(model_y=RandomForestRegressor(random_state=42),
                                  model_t=RandomForestClassifier(min_samples_leaf=10, random_state=42),
                                   discrete_treatment=True, cv=3, random_state=123
                                )
econml_causalForest.fit(Y=y_train, T=T_train, X=X_train, W=None)
print(f'econml_ATE_forest: {econml_causalForest.ate(X_test, T0=0, T1=5)}')

print(econml_causalForest.summary())
print(econml_causalForest.ate_inference(X))

Got the results as follows

 Doubly Robust ATE on Training Data Results          
==============================================================
         point_estimate stderr zstat  pvalue ci_lower ci_upper
--------------------------------------------------------------
ATE|T0_1          0.128   0.02  6.402    0.0    0.089    0.167
ATE|T0_2          0.143  0.019  7.596    0.0    0.106     0.18
ATE|T0_3          0.164   0.02   8.35    0.0    0.126    0.203
ATE|T0_5          0.313   0.02 15.827    0.0    0.274    0.352


econml_ATE_forest: 0.27076799164408494
               Uncertainty of Mean Point Estimate              
===============================================================
mean_point stderr_mean zstat pvalue ci_mean_lower ci_mean_upper
---------------------------------------------------------------
     0.109       1.059 0.103  0.918        -1.968         2.185
      Distribution of Point Estimate     
=========================================
std_point pct_point_lower pct_point_upper
-----------------------------------------
    0.946          -0.263           0.233
     Total Variance of Point Estimate     
==========================================
stderr_point ci_point_lower ci_point_upper
------------------------------------------
       1.421         -0.374          0.377
------------------------------------------

Which results should i take into consideration Doubly Robust or DoublML. Both ATE estimates are different ? And how should i intrepret the ATE and CI?

Start with the CausalForestDML documentation and the shown ate, summary, and ate_inference calls. Compare what each method estimates and how their confidence intervals are defined, then document which output applies to the question and how to interpret the ATE and interval.