From 6b2b9a81551d8adb92ca8e546f81414db130603b Mon Sep 17 00:00:00 2001
From: Jeong-Yoon Lee <jeongyoon.lee1@gmail.com>
Date: Sat, 23 Mar 2024 08:07:31 -0700
Subject: [PATCH] remove undefined variables in match.__main__() (#749)

---
 causalml/match.py | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/causalml/match.py b/causalml/match.py
index 1fe5cd77..9bde5872 100644
--- a/causalml/match.py
+++ b/causalml/match.py
@@ -424,18 +424,22 @@ def search_best_match(self, df):
 
 
 if __name__ == "__main__":
-    from .features import TREATMENT_COL, SCORE_COL, GROUPBY_COL, PROPENSITY_FEATURES
-    from .features import PROPENSITY_FEATURE_TRANSFORMATIONS, MATCHING_COVARIATES
     from .features import load_data
     from .propensity import ElasticNetPropensityModel
 
+    TREATMENT_COL = "treatment"
+    SCORE_COL = "score"
+    GROUPBY_COL = "group"
+
     parser = argparse.ArgumentParser()
     parser.add_argument("--input-file", required=True, dest="input_file")
     parser.add_argument("--output-file", required=True, dest="output_file")
     parser.add_argument("--treatment-col", default=TREATMENT_COL, dest="treatment_col")
     parser.add_argument("--groupby-col", default=GROUPBY_COL, dest="groupby_col")
+    parser.add_argument("--score-col", default=SCORE_COL, dest="score_col")
+    parser.add_argument("--feature-cols", nargs="+", required=True, dest="feature_cols")
     parser.add_argument(
-        "--feature-cols", nargs="+", default=PROPENSITY_FEATURES, dest="feature_cols"
+        "--matching-cols", nargs="+", required=True, dest="matching_cols"
     )
     parser.add_argument("--caliper", type=float, default=0.2)
     parser.add_argument("--replace", default=False, action="store_true")
@@ -455,16 +459,15 @@ def search_best_match(self, df):
     X = load_data(
         data=df,
         features=args.feature_cols,
-        transformations=PROPENSITY_FEATURE_TRANSFORMATIONS,
     )
 
     logger.info("Scoring with a propensity model: {}".format(pm))
-    df[SCORE_COL] = pm.fit_predict(X, w)
+    df[args.score_col] = pm.fit_predict(X, w)
 
     logger.info(
         "Balance before matching:\n{}".format(
             create_table_one(
-                data=df, treatment_col=args.treatment_col, features=MATCHING_COVARIATES
+                data=df, treatment_col=args.treatment_col, features=args.matching_cols
             )
         )
     )
@@ -475,7 +478,7 @@ def search_best_match(self, df):
     matched = psm.match_by_group(
         data=df,
         treatment_col=args.treatment_col,
-        score_cols=[SCORE_COL],
+        score_cols=[args.score_col],
         groupby_col=args.groupby_col,
     )
     logger.info("shape: {}\n{}".format(matched.shape, matched.head()))
@@ -485,7 +488,7 @@ def search_best_match(self, df):
             create_table_one(
                 data=matched,
                 treatment_col=args.treatment_col,
-                features=MATCHING_COVARIATES,
+                features=args.matching_cols,
             )
         )
     )