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PoC: Modify single IV to accept nans #1296

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PoC: Modify single IV to accept nans #1296

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13 changes: 7 additions & 6 deletions dowhy/causal_estimators/instrumental_variable_estimator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -139,17 +139,18 @@ def estimate_effect(
instrument_is_binary = num_unique_values <= 2
if instrument_is_binary:
# Obtain estimate by Wald Estimator
y1_z = np.mean(data[self._target_estimand.outcome_variable[0]][instrument == 1])
y0_z = np.mean(data[self._target_estimand.outcome_variable[0]][instrument == 0])
x1_z = np.mean(data[self._target_estimand.treatment_variable[0]][instrument == 1])
x0_z = np.mean(data[self._target_estimand.treatment_variable[0]][instrument == 0])
y1_z = np.nanmean(data[self._target_estimand.outcome_variable[0]][instrument == 1])
y0_z = np.nanmean(data[self._target_estimand.outcome_variable[0]][instrument == 0])
x1_z = np.nanmean(data[self._target_estimand.treatment_variable[0]][instrument == 1])
x0_z = np.nanmean(data[self._target_estimand.treatment_variable[0]][instrument == 0])
num = y1_z - y0_z
deno = x1_z - x0_z
iv_est = num / deno
else:
# Obtain estimate by 2SLS estimator: Cov(y,z) / Cov(x,z)
num_yz = np.cov(data[self._target_estimand.outcome_variable[0]], instrument)[0, 1]
deno_xz = np.cov(data[self._target_estimand.treatment_variable[0]], instrument)[0, 1]
masked_data = np.ma.masked_invalid(data[[self._target_estimand.outcome_variable[0], self._target_estimand.treatment_variable[0], self.estimating_instrument_names[0]]])
num_yz = np.ma.cov(masked_data[:, 0], masked_data[: ,2])[0, 1]
deno_xz = np.ma.cov(masked_data[:, 1], masked_data[:, 2])[0, 1]
iv_est = num_yz / deno_xz
else:
# More than 1 instrument. Use 2sls.
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