Remove maq dependency

causalml/metrics/__init__.py

@@ -32,4 +32,3 @@
     SensitivitySubsetData,
     SensitivitySelectionBias,
 )  # noqa
-from maq import MAQ, get_ipw_scores  # noqa

pyproject.toml

@@ -45,7 +45,6 @@ dependencies = [
     "torch",
     "pyro-ppl",
     "graphviz",
-    "maq@git+https://github.com/grf-labs/[email protected]#egg=maq&subdirectory=python-package",
 ]
 
 [project.optional-dependencies]

tests/test_metrics.py

@@ -1,8 +1,6 @@
 import pandas as pd
-import numpy as np
 from numpy import isclose
 from causalml.metrics.visualize import qini_score
-from causalml.metrics import MAQ, get_ipw_scores
 
 
 def test_qini_score():
@@ -28,18 +26,3 @@ def test_qini_score():
     # for each learner, its qini score should stay same no matter calling with another model or calling separately
     assert isclose(full_result["learner_1"], learner_1_result["learner_1"])
     assert isclose(full_result["learner_2"], learner_2_result["learner_2"])
-
-
-def test_MAQ():
-    np.random.seed(42)
-    n = 1000
-    K = 5
-    tau_hat = np.random.randn(n, K)
-    cost = np.random.rand(n, K)
-    DR_scores = np.random.randn(n, K)
-
-    mq = MAQ(n_bootstrap=200)
-    mq.fit(tau_hat, cost, DR_scores)
-
-    #    (0.005729002695991717, 0.019814651108894354)
-    assert isclose(mq.average_gain(spend=0.1)[0], 0.005729)