adding datasets

Signed-off-by: Nathaniel <[email protected]>

Author: NathanielF

causalpy/data/datasets.py

@@ -38,6 +38,8 @@
     "nhefs": {"filename": "nhefs.csv"},
     "schoolReturns": {"filename": "schoolingReturns.csv"},
     "pisa18": {"filename": "PISA18sampleScale.csv"},
+    "nets": {"filename": "nets_df.csv"},
+    "lalonde": {"filename": "lalonde.csv"},
 }
 
 

causalpy/pymc_models.py

@@ -581,13 +581,12 @@ def fit_outcome_model(
         -----
         - This model uses a sampled version of the propensity score (`p`) from the
         posterior of the treatment model, randomly selecting one posterior draw
-        per call.
+        per call. This term is estimated initially in the InversePropensity
+        class initialisation.
         - The term `beta_ps[0] * p + beta_ps[1] * (p * treatment)` captures both
         main and interaction effects of the propensity score.
         - Including spline adjustment enables modeling nonlinear relationships
         between the propensity score and the outcome.
-        - Compatible with IPW-style estimation when combined with weighted loss or
-        diagnostics outside this function.
 
         """
         if not hasattr(self, "idata"):