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Suppress misleading base treatment warning (option 1) #784

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Suppress misleading base treatment warning (option 1) #784

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b221396
Suppress warning if base treatment is default scalar
FlorianNachtigall Jun 23, 2023
9a5bfbb
Remove dead code
FlorianNachtigall Jun 23, 2023
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9 changes: 5 additions & 4 deletions econml/_cate_estimator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -591,15 +591,16 @@ 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)
# if X is None then the shape of const_marginal_effect will be wrong because the number
# 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:
Expand Down Expand Up @@ -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.")
Expand Down