Update Data Backend and dataset paths

monte-cover/src/montecover/irm/apo.py

@@ -3,7 +3,7 @@
 import doubleml as dml
 import numpy as np
 import pandas as pd
-from doubleml.datasets import make_irm_data_discrete_treatments
+from doubleml.irm.datasets import make_irm_data_discrete_treatments
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/irm/apos.py

@@ -3,7 +3,7 @@
 import doubleml as dml
 import numpy as np
 import pandas as pd
-from doubleml.datasets import make_irm_data_discrete_treatments
+from doubleml.irm.datasets import make_irm_data_discrete_treatments
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/irm/iivm_late.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, Optional
 
 import doubleml as dml
-from doubleml.datasets import make_iivm_data
+from doubleml.irm.datasets import make_iivm_data
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/irm/irm_ate.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, Optional
 
 import doubleml as dml
-from doubleml.datasets import make_irm_data
+from doubleml.irm.datasets import make_irm_data
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/irm/irm_ate_sensitivity.py

@@ -3,7 +3,7 @@
 import doubleml as dml
 import numpy as np
 import pandas as pd
-from doubleml.datasets import make_confounded_irm_data
+from doubleml.irm.datasets import make_confounded_irm_data
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/irm/irm_atte.py

@@ -2,7 +2,7 @@
 
 import doubleml as dml
 import numpy as np
-from doubleml.datasets import make_irm_data
+from doubleml.irm.datasets import make_irm_data
 from scipy.linalg import toeplitz
 
 from montecover.base import BaseSimulation

monte-cover/src/montecover/irm/irm_atte_sensitivity.py

@@ -3,7 +3,7 @@
 import doubleml as dml
 import numpy as np
 import pandas as pd
-from doubleml.datasets import make_confounded_irm_data
+from doubleml.irm.datasets import make_confounded_irm_data
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/irm/irm_cate.py

@@ -4,7 +4,7 @@
 import numpy as np
 import pandas as pd
 import patsy
-from doubleml.datasets import make_heterogeneous_data
+from doubleml.irm.datasets import make_heterogeneous_data
 from sklearn.linear_model import LinearRegression
 
 from montecover.base import BaseSimulation

monte-cover/src/montecover/irm/irm_gate.py

@@ -3,7 +3,7 @@
 import doubleml as dml
 import numpy as np
 import pandas as pd
-from doubleml.datasets import make_heterogeneous_data
+from doubleml.irm.datasets import make_heterogeneous_data
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/plm/pliv_late.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, Optional
 
 import doubleml as dml
-from doubleml.datasets import make_pliv_CHS2015
+from doubleml.plm.datasets import make_pliv_CHS2015
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/plm/plr_ate.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, Optional
 
 import doubleml as dml
-from doubleml.datasets import make_plr_CCDDHNR2018
+from doubleml.plm.datasets import make_plr_CCDDHNR2018
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/plm/plr_ate_sensitivity.py

@@ -3,7 +3,7 @@
 import doubleml as dml
 import numpy as np
 import pandas as pd
-from doubleml.datasets import make_confounded_plr_data
+from doubleml.plm.datasets import make_confounded_plr_data
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/plm/plr_cate.py

@@ -4,7 +4,7 @@
 import numpy as np
 import pandas as pd
 import patsy
-from doubleml.datasets import make_heterogeneous_data
+from doubleml.irm.datasets import make_heterogeneous_data
 from sklearn.linear_model import LinearRegression
 
 from montecover.base import BaseSimulation

monte-cover/src/montecover/plm/plr_gate.py

@@ -3,7 +3,7 @@
 import doubleml as dml
 import numpy as np
 import pandas as pd
-from doubleml.datasets import make_heterogeneous_data
+from doubleml.irm.datasets import make_heterogeneous_data
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config

monte-cover/src/montecover/rdd/rdd.py

@@ -108,7 +108,7 @@ def _rdrobust_benchmark(self, dml_data, dml_params, i_rep):
         """Run a benchmark using rdrobust for RDD."""
 
         # Extract parameters
-        score = dml_data.data[dml_data.s_col]
+        score = dml_data.data[dml_data.score_col]
         Y = dml_data.data[dml_data.y_col]
         Z = dml_data.data[dml_data.x_cols]
 
@@ -230,10 +230,15 @@ def _generate_dml_data(self, dgp_params) -> dml.DoubleMLData:
             cutoff=dgp_params["cutoff"],
         )
 
-        score = data["score"]
-        Y = data["Y"]
-        X = data["X"].reshape(dgp_params["n_obs"], -1)
-        D = data["D"]
+        x_cols = ["x" + str(i) for i in range(data["X"].shape[1])]
+        columns = ["y", "d", "score"] + x_cols
+        df = pd.DataFrame(np.column_stack((data["Y"], data["D"], data["score"], data["X"])), columns=columns)
 
-        dml_data = dml.DoubleMLData.from_arrays(y=Y, d=D, x=X, s=score)
+        dml_data = dml.data.DoubleMLRDDData(
+            data=df,
+            y_col="y",
+            d_cols=["d"],
+            x_cols=x_cols,
+            score_col="score",
+        )
         return dml_data

monte-cover/src/montecover/ssm/ssm_mar_ate.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, Optional
 
 import doubleml as dml
-from doubleml.datasets import make_ssm_data
+from doubleml.irm.datasets import make_ssm_data
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config
@@ -119,5 +119,5 @@ def _generate_dml_data(self, dgp_params: Dict[str, Any]) -> dml.DoubleMLData:
             mar=True,
             return_type="DataFrame",
         )
-        dml_data = dml.DoubleMLData(data, "y", "d", s_col="s")
+        dml_data = dml.data.DoubleMLSSMData(data, "y", "d", s_col="s")
         return dml_data

monte-cover/src/montecover/ssm/ssm_nonig_ate.py

@@ -1,7 +1,7 @@
 from typing import Any, Dict, Optional
 
 import doubleml as dml
-from doubleml.datasets import make_ssm_data
+from doubleml.irm.datasets import make_ssm_data
 
 from montecover.base import BaseSimulation
 from montecover.utils import create_learner_from_config
@@ -121,5 +121,5 @@ def _generate_dml_data(self, dgp_params: Dict[str, Any]) -> dml.DoubleMLData:
             mar=False,
             return_type="DataFrame",
         )
-        dml_data = dml.DoubleMLData(data, "y", "d", z_cols="z", s_col="s")
+        dml_data = dml.data.DoubleMLSSMData(data, "y", "d", z_cols="z", s_col="s")
         return dml_data

results/did/did_cs_atte_coverage.csv

@@ -1,16 +1,16 @@
 Learner g,Learner m,Score,In-sample-norm.,DGP,level,Coverage,CI Length,Bias,repetition
-LGBM,LGBM,experimental,False,1,0.9,0.714,10.421426989395727,3.905582429463871,1000
-LGBM,LGBM,experimental,False,1,0.95,0.796,12.417896177537456,3.905582429463871,1000
-LGBM,LGBM,experimental,False,2,0.9,0.747,11.153655901082034,3.716762206006709,1000
-LGBM,LGBM,experimental,False,2,0.95,0.83,13.290400740757454,3.716762206006709,1000
-LGBM,LGBM,experimental,False,3,0.9,0.826,10.129500599367143,2.9806910089316485,1000
-LGBM,LGBM,experimental,False,3,0.95,0.9,12.070044428775317,2.9806910089316485,1000
-LGBM,LGBM,experimental,False,4,0.9,0.709,10.248410164509226,3.911148948219506,1000
-LGBM,LGBM,experimental,False,4,0.95,0.788,12.211733914865182,3.911148948219506,1000
-LGBM,LGBM,experimental,False,5,0.9,0.897,11.953436462004694,2.904688869350282,1000
-LGBM,LGBM,experimental,False,5,0.95,0.95,14.243398058730905,2.904688869350282,1000
-LGBM,LGBM,experimental,False,6,0.9,0.901,10.409876930645252,2.475898589693061,1000
-LGBM,LGBM,experimental,False,6,0.95,0.951,12.40413343366814,2.475898589693061,1000
+LGBM,LGBM,experimental,False,1,0.9,0.715,10.421234124600447,3.9053554919818962,1000
+LGBM,LGBM,experimental,False,1,0.95,0.796,12.417666364959342,3.9053554919818962,1000
+LGBM,LGBM,experimental,False,2,0.9,0.746,11.1534791019021,3.716868972461292,1000
+LGBM,LGBM,experimental,False,2,0.95,0.83,13.29019007154076,3.716868972461292,1000
+LGBM,LGBM,experimental,False,3,0.9,0.826,10.129283998917318,2.9807419839606,1000
+LGBM,LGBM,experimental,False,3,0.95,0.899,12.069786333420366,2.9807419839606,1000
+LGBM,LGBM,experimental,False,4,0.9,0.709,10.248213641174399,3.911145887601952,1000
+LGBM,LGBM,experimental,False,4,0.95,0.789,12.211499742868313,3.911145887601952,1000
+LGBM,LGBM,experimental,False,5,0.9,0.897,11.95322581221267,2.905111352900416,1000
+LGBM,LGBM,experimental,False,5,0.95,0.949,14.243147054022064,2.905111352900416,1000
+LGBM,LGBM,experimental,False,6,0.9,0.9,10.409644052784493,2.475811487308378,1000
+LGBM,LGBM,experimental,False,6,0.95,0.951,12.403855942581755,2.475811487308378,1000
 LGBM,LGBM,experimental,True,1,0.9,0.695,10.441642571924747,3.98549935766534,1000
 LGBM,LGBM,experimental,True,1,0.95,0.774,12.441984529859,3.98549935766534,1000
 LGBM,LGBM,experimental,True,2,0.9,0.769,11.14737947150305,3.7228962496196263,1000
@@ -23,18 +23,18 @@ LGBM,LGBM,experimental,True,5,0.9,0.894,11.981860540543671,2.9439981898378322,10
 LGBM,LGBM,experimental,True,5,0.95,0.949,14.277267437329282,2.9439981898378322,1000
 LGBM,LGBM,experimental,True,6,0.9,0.894,10.42424549288115,2.562430198965583,1000
 LGBM,LGBM,experimental,True,6,0.95,0.955,12.421254631585413,2.562430198965583,1000
-LGBM,LGBM,observational,False,1,0.9,0.94,50.01837238134115,11.670635965681225,1000
-LGBM,LGBM,observational,False,1,0.95,0.973,59.600566777747616,11.670635965681225,1000
-LGBM,LGBM,observational,False,2,0.9,0.929,59.19235508827008,13.470175038952636,1000
-LGBM,LGBM,observational,False,2,0.95,0.977,70.53204141217991,13.470175038952636,1000
-LGBM,LGBM,observational,False,3,0.9,0.945,56.62260255116421,12.634763113659828,1000
-LGBM,LGBM,observational,False,3,0.95,0.989,67.46999240102099,12.634763113659828,1000
-LGBM,LGBM,observational,False,4,0.9,0.945,70.02798665966547,16.708878014378698,1000
-LGBM,LGBM,observational,False,4,0.95,0.982,83.44349279101235,16.708878014378698,1000
-LGBM,LGBM,observational,False,5,0.9,0.932,32.68395008367948,7.535531362351606,1000
-LGBM,LGBM,observational,False,5,0.95,0.973,38.945328621880215,7.535531362351606,1000
-LGBM,LGBM,observational,False,6,0.9,0.922,31.254676611393744,7.328062886784694,1000
-LGBM,LGBM,observational,False,6,0.95,0.96,37.24224423562365,7.328062886784694,1000
+LGBM,LGBM,observational,False,1,0.9,0.94,50.016950326053895,11.669744501027184,1000
+LGBM,LGBM,observational,False,1,0.95,0.973,59.598872294359325,11.669744501027184,1000
+LGBM,LGBM,observational,False,2,0.9,0.929,59.192241716928855,13.469752811318875,1000
+LGBM,LGBM,observational,False,2,0.95,0.977,70.53190632189465,13.469752811318875,1000
+LGBM,LGBM,observational,False,3,0.9,0.945,56.62183695590324,12.633898481806959,1000
+LGBM,LGBM,observational,False,3,0.95,0.989,67.46908013800035,12.633898481806959,1000
+LGBM,LGBM,observational,False,4,0.9,0.946,70.02795549101278,16.708529185685062,1000
+LGBM,LGBM,observational,False,4,0.95,0.982,83.44345565127193,16.708529185685062,1000
+LGBM,LGBM,observational,False,5,0.9,0.933,32.68491159413625,7.535384184190604,1000
+LGBM,LGBM,observational,False,5,0.95,0.973,38.94647433225539,7.535384184190604,1000
+LGBM,LGBM,observational,False,6,0.9,0.922,31.255938663097147,7.3282010953989065,1000
+LGBM,LGBM,observational,False,6,0.95,0.96,37.24374806298236,7.3282010953989065,1000
 LGBM,LGBM,observational,True,1,0.9,0.903,17.911052050251026,4.470376853620159,1000
 LGBM,LGBM,observational,True,1,0.95,0.954,21.342334885309523,4.470376853620159,1000
 LGBM,LGBM,observational,True,2,0.9,0.928,20.466840035852762,4.861276719991755,1000

results/did/did_cs_atte_coverage_metadata.csv

@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.11.dev0,did_cs_atte_coverage.py,2025-06-06 09:10:00,12688.770802021027,3.12.3
+0.11.dev0,did_cs_atte_coverage.py,2025-09-08 10:19:25,13230.593134403229,3.12.3