diff --git a/econml/_cate_estimator.py b/econml/_cate_estimator.py
index 3c453b291..be559ad8e 100644
--- a/econml/_cate_estimator.py
+++ b/econml/_cate_estimator.py
@@ -591,7 +591,9 @@ def effect(self, X=None, *, T0, T1):
             Note that when Y is a vector rather than a 2-dimensional array, the corresponding
             singleton dimension will be collapsed (so this method will return a vector)
         """
-        X, T0, T1 = self._expand_treatments(X, T0, T1)
+        X, T1 = self._expand_treatments(X, T1)
+        is_default = ndim(T0) == 0 and T0 == 0
+        _, T0 = self._expand_treatments(None, T0, suppress_warn=is_default)
         # TODO: what if input is sparse? - there's no equivalent to einsum,
         #       but tensordot can't be applied to this problem because we don't sum over m
         eff = self.const_marginal_effect(X)
@@ -599,7 +601,6 @@ def effect(self, X=None, *, T0, T1):
         # of rows of T was not taken into account
         if X is None:
             eff = np.repeat(eff, shape(T0)[0], axis=0)
-        m = shape(eff)[0]
         dT = T1 - T0
         einsum_str = 'myt,mt->my'
         if ndim(dT) == 1:
@@ -847,12 +848,12 @@ def _postfit(self, Y, T, *args, **kwargs):
         if self.transformer:
             self._set_transformed_treatment_names()
 
-    def _expand_treatments(self, X=None, *Ts, transform=True):
+    def _expand_treatments(self, X=None, *Ts, transform=True, suppress_warn=False):
         X, *Ts = check_input_arrays(X, *Ts)
         n_rows = 1 if X is None else shape(X)[0]
         outTs = []
         for T in Ts:
-            if (ndim(T) == 0) and self._d_t_in and self._d_t_in[0] > 1:
+            if (ndim(T) == 0) and self._d_t_in and self._d_t_in[0] > 1 and not suppress_warn:
                 warn("A scalar was specified but there are multiple treatments; "
                      "the same value will be used for each treatment.  Consider specifying"
                      "all treatments, or using the const_marginal_effect method.")