Fix 2sls marginal effect

econml/tests/test_two_stage_least_squares.py

@@ -65,9 +65,9 @@ def make_random(d):
             sz = (n, d) if d >= 0 else (n,)
             return np.random.normal(size=sz)
 
-        for d_t in [1, 2]:
+        for d_t in [-1, 1, 2]:
             n_t = d_t if d_t > 0 else 1
-            for d_y in [1, 2]:
+            for d_y in [-1, 1, 2]:
                 for d_x in [1, 5]:
                     for d_z in [1, 2]:
                         d_w = 1
@@ -80,9 +80,18 @@ def make_random(d):
                                 dt_featurizer=DPolynomialFeatures())
 
                             est.fit(Y, T, X, W, Z)
+
                             eff = est.effect(X)
                             marg_eff = est.marginal_effect(T, X)
 
+                            effect_shape = (n,) + ((d_y,) if d_y > 0 else ())
+                            marginal_effect_shape = ((n if d_x else 1,) +
+                                                     ((d_y,) if d_y > 0 else ()) +
+                                                     ((d_t,) if d_t > 0 else()))
+
+                            self.assertEqual(shape(marg_eff), marginal_effect_shape)
+                            self.assertEqual(shape(eff), effect_shape)
+
     def test_marg_eff(self):
         X = np.random.normal(size=(5000, 2))
         Z = np.random.normal(size=(5000, 2))

econml/two_stage_least_squares.py

@@ -235,11 +235,12 @@ def fit(self, Y, T, X, W, Z, inference=None):
             W = np.empty((shape(Y)[0], 0))
         assert shape(Y)[0] == shape(T)[0] == shape(X)[0] == shape(W)[0] == shape(Z)[0]
 
+        # make T 2D if if was a vector
+        if ndim(T) == 1:
+            T = reshape(T, (-1, 1))
+
         # store number of columns of W so that we can create correctly shaped zero array in effect and marginal effect
         self._d_w = shape(W)[1]
-        # store number of columns of T so that we can pass scalars to effect
-        # TODO: support vector T and Y
-        self._d_t = shape(T)[1]
 
         # two stage approximation
         # first, get basis expansions of T, X, and Z
@@ -285,9 +286,13 @@ def effect(self, X=None, T0=0, T1=1):
 
         """
         if ndim(T0) == 0:
-            T0 = np.full((1 if X is None else shape(X)[0], self._d_t), T0)
+            T0 = np.full((1 if X is None else shape(X)[0],) + self._d_t, T0)
         if ndim(T1) == 0:
-            T1 = np.full((1 if X is None else shape(X)[0], self._d_t), T1)
+            T1 = np.full((1 if X is None else shape(X)[0],) + self._d_t, T1)
+        if ndim(T0) == 1:
+            T0 = reshape(T0, (-1, 1))
+        if ndim(T1) == 1:
+            T1 = reshape(T1, (-1, 1))
         if X is None:
             X = np.empty((shape(T0)[0], 0))
         assert shape(T0) == shape(T1)
@@ -329,7 +334,7 @@ def marginal_effect(self, T, X=None):
 
         ft_X = self._x_featurizer.transform(X)
         n = shape(T)[0]
-        dT = self._dt_featurizer.transform(T)
+        dT = self._dt_featurizer.transform(T if ndim(T) == 2 else reshape(T, (-1, 1)))
         W = np.zeros((size(T), self._d_w))
         # dT should be an n×dₜ×fₜ array (but if T was a vector, or if there is only one feature,
         # dT may be only 2-dimensional)
@@ -342,4 +347,8 @@ def marginal_effect(self, T, X=None):
         features = transpose(features, [0, 1, 3, 2])  # swap last two dims to match cross_product
         features = reshape(features, (size(T), -1))
         output = self._model_Y.predict(_add_zeros(np.hstack([W, features])))
-        return reshape(output, shape(T) + (shape(output)[-1],))
+        output = reshape(output, shape(T) + shape(output)[1:])
+        if ndim(output) == 3:
+            return transpose(output, (0, 2, 1))  # transpose trailing T and Y dims
+        else:
+            return output