support target treatment indicator other than 1

Author: TomeHirata

dte_adj/stratified.py

@@ -73,10 +73,9 @@ def _compute_cumulative_distribution(
         for s in s_list:
             s_mask = strata == s
             w_s[s] = (s_mask & treatment_mask).sum() / s_mask.sum()
-        for i, outcome in enumerate(locations):
-            for j in range(n_records):
-                s = strata[j]
-                prediction[j, i] = (outcomes[j] <= outcome) / w_s[s] * treatment_mask[j]
+        for j in range(n_records):
+            s = strata[j]
+            prediction[j] = (outcomes[j] <= locations) / w_s[s] * treatment_mask[j]
 
         unconditional_pred = {s: prediction[s == strata].mean(axis=0) for s in s_list}
         conditional_prediction = np.array([unconditional_pred[s] for s in strata])
@@ -263,9 +262,9 @@ def _compute_cumulative_distribution(
                     covariates_train = covariates[fold_mask]
                     binomial_train = binomial[fold_mask]
                     # Pool the records across strata and train the model
-                    if len(np.unique(binomial_train)) > 1:
-                        self.model = deepcopy(self.base_model)
-                        self.model.fit(covariates_train, binomial_train)
+                    # if len(np.unique(binomial_train)) > 1:
+                    #     self.model = deepcopy(self.base_model)
+                    #     self.model.fit(covariates_train, binomial_train)
                     for s in s_list:
                         s_mask = strata == s
                         weight = (s_mask & treatment_mask).sum() / s_mask.sum()
@@ -275,8 +274,8 @@ def _compute_cumulative_distribution(
                         binomial_train = binomial[subset_train_mask]
                         # TODO: revisit the logic here
                         if len(np.unique(binomial_train)) > 1:
-                            # self.model = deepcopy(self.base_model)
-                            # self.model.fit(covariates_train, binomial_train)
+                            self.model = deepcopy(self.base_model)
+                            self.model.fit(covariates_train, binomial_train)
                             pass
                         else:
                             pred = binomial_train[0]

dte_adj/util.py

@@ -1,6 +1,12 @@
 import numpy as np
 from scipy.stats import norm
-from typing import Tuple
+from typing import Tuple, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from dte_adj.local import (
+        SimpleStratifiedDistributionEstimator,
+        AdjustedLocalDistributionEstimator,
+    )
 
 
 def compute_confidence_intervals(
@@ -110,7 +116,7 @@ def compute_confidence_intervals(
 
 
 def _compute_local_treatment_effects_core(
-    estimator,
+    estimator: "SimpleStratifiedDistributionEstimator | AdjustedLocalDistributionEstimator",
     target_treatment_arm: int,
     control_treatment_arm: int,
     locations: np.ndarray,
@@ -149,10 +155,10 @@ def _compute_local_treatment_effects_core(
 
     # Compute treatment propensity (probability of treatment)
     d_t_prediction, d_t_psi, d_t_eta = estimator._compute_cumulative_distribution(
-        target_treatment_arm, np.zeros(1), X, Z, 1 - D
+        target_treatment_arm, np.zeros(1), X, Z, 1 - (target_treatment_arm == D)
     )
     d_c_prediction, d_c_psi, d_c_eta = estimator._compute_cumulative_distribution(
-        control_treatment_arm, np.zeros(1), X, Z, 1 - D
+        control_treatment_arm, np.zeros(1), X, Z, 1 - (target_treatment_arm == D)
     )
 
     # Compute outcome distributions (different for LDTE vs LPTE)
@@ -257,7 +263,7 @@ def xi(s):
 
 
 def compute_ldte(
-    estimator,
+    estimator: "SimpleStratifiedDistributionEstimator | AdjustedLocalDistributionEstimator",
     target_treatment_arm: int,
     control_treatment_arm: int,
     locations: np.ndarray,
@@ -290,7 +296,7 @@ def compute_ldte(
 
 
 def compute_lpte(
-    estimator,
+    estimator: "SimpleStratifiedDistributionEstimator | AdjustedLocalDistributionEstimator",
     target_treatment_arm: int,
     control_treatment_arm: int,
     locations: np.ndarray,

example/example.ipynb

@@ -278,9 +278,7 @@
    ],
    "source": [
     "pte, lower_bound, upper_bound = estimator.predict_pte(\n",
-    "    target_treatment_arm=1,\n",
-    "    control_treatment_arm=0,\n",
-    "    locations=locations\n",
+    "    target_treatment_arm=1, control_treatment_arm=0, locations=locations\n",
     ")\n",
     "plot(\n",
     "    locations[:-1],\n",