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Vectorize operations for propensity score matching #1179

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merged 2 commits into from
Jun 4, 2024
Merged

Vectorize operations for propensity score matching #1179

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0313d16
Add vector operations
rahulbshrestha May 12, 2024
abe5ba1
Vectorize remaining list
rahulbshrestha May 14, 2024
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30 changes: 16 additions & 14 deletions dowhy/causal_estimators/propensity_score_matching_estimator.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
from typing import Any, List, Optional, Union

import numpy as np
import pandas as pd
from sklearn.neighbors import NearestNeighbors

Expand Down Expand Up @@ -120,7 +121,7 @@ def estimate_effect(

# TODO remove neighbors that are more than a given radius apart

# estimate ATT on treated by summing over difference between matched neighbors
# Estimating ATT on treated by summing over difference between matched neighbors
control_neighbors = NearestNeighbors(n_neighbors=1, algorithm="ball_tree").fit(
control[self.propensity_score_column].values.reshape(-1, 1)
)
Expand All @@ -129,27 +130,28 @@ def estimate_effect(
self.logger.debug(distances)

att = 0
numtreatedunits = treated.shape[0]
for i in range(numtreatedunits):
treated_outcome = treated.iloc[i][self._target_estimand.outcome_variable[0]].item()
control_outcome = control.iloc[indices[i]][self._target_estimand.outcome_variable[0]].item()
att += treated_outcome - control_outcome
outcome_variable = self._target_estimand.outcome_variable[0]
treated_outcomes = treated[outcome_variable]
control_outcomes = list(control.iloc[indices.flatten()][outcome_variable])

att = (treated_outcomes - control_outcomes).mean()

att /= numtreatedunits
# Estimating ATC

# Now computing ATC
treated_neighbors = NearestNeighbors(n_neighbors=1, algorithm="ball_tree").fit(
treated[self.propensity_score_column].values.reshape(-1, 1)
)
distances, indices = treated_neighbors.kneighbors(control[self.propensity_score_column].values.reshape(-1, 1))

atc = 0
numcontrolunits = control.shape[0]
for i in range(numcontrolunits):
control_outcome = control.iloc[i][self._target_estimand.outcome_variable[0]].item()
treated_outcome = treated.iloc[indices[i]][self._target_estimand.outcome_variable[0]].item()
atc += treated_outcome - control_outcome
outcome_variable = self._target_estimand.outcome_variable[0]
control_outcomes = control[outcome_variable]
treated_outcomes = list(treated.iloc[indices.flatten()][outcome_variable])

atc /= numcontrolunits
atc = (treated_outcomes - control_outcomes).mean()

numtreatedunits = treated.shape[0]
numcontrolunits = control.shape[0]

if target_units == "att":
est = att
Expand Down