Update PLR ATE simulation configurations and results

- Adjusted the number of repetitions from 200 to 100 in tuning configuration.
- Increased the number of units from 250 to 1000 in both main and tuning configurations.
- Updated metadata files to reflect new runtime and simulation parameters.
- Modified coverage results to include new entries based on updated configurations.

Author: SvenKlaassen

doc/plm/plpr.qmd

@@ -25,7 +25,7 @@ init_notebook_mode(all_interactive=True)
 
 ## Coverage
 
-The simulations are based on the  the [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $250$ units and $10$ time periods. The following DGPs are considered:
+The simulations are based on the  the [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $1000$ units and $10$ time periods. The following DGPs are considered:
 
  - DGP 1: Linear in the nuisance parameters
  - DGP 2: Non-linear and smooth in the nuisance parameters
@@ -122,10 +122,9 @@ generate_and_show_styled_table(
 
 ## Tuning
 
-The simulations are based on the  the [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $250$ units and $10$ time periods. The following DGPs are considered:
+The simulations are based on the  the [make_plpr_CP2025](https://docs.doubleml.org/stable/api/datasets.html#dataset-generators)-DGP with $1000$ units and $10$ time periods. The following DGPs are considered:
 
  - DGP 1: Linear in the nuisance parameters
- - DGP 2: Non-linear and smooth in the nuisance parameters
  - DGP 3: Non-linear and discontinuous in the nuisance parameters
 
 This is only an example as the untuned version just relies on the default configuration.

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

@@ -98,9 +98,9 @@ def run_single_rep(self, dml_data, dml_params) -> Dict[str, Any]:
             for level in self.confidence_parameters["level"]:
                 level_result = dict()
                 level_result["coverage"] = self._compute_coverage(
-                    thetas=dml_model.coef,
+                    thetas=model.coef,
                     oracle_thetas=self.oracle_values["theta"],
-                    confint=dml_model.confint(level=level),
+                    confint=model.confint(level=level),
                     joint_confint=None,
                 )
 

results/plm/plpr_ate_config.yml

@@ -9,7 +9,7 @@ dgp_parameters:
   dim_x:
   - 10
   num_id:
-  - 250
+  - 1000
   num_t:
   - 10
   DGP:

results/plm/plpr_ate_coverage.csv

@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
-0.11.2.dev96,PLPRATECoverageSimulation,2026-01-15 22:20,21.816437701384228,3.12.9,scripts/plm/plpr_ate_config.yml
+0.11.2.dev96,PLPRATECoverageSimulation,2026-01-16 15:02,36.614749721686046,3.12.9,scripts/plm/plpr_ate_config.yml

results/plm/plpr_ate_metadata.csv

@@ -1,5 +1,5 @@
 simulation_parameters:
-  repetitions: 200
+  repetitions: 100
   max_runtime: 19800
   random_seed: 42
   n_jobs: -2
@@ -9,12 +9,11 @@ dgp_parameters:
   dim_x:
   - 10
   num_id:
-  - 250
+  - 1000
   num_t:
   - 10
   DGP:
   - dgp1
-  - dgp2
   - dgp3
 learner_definitions:
   lgbm: &id001

results/plm/plpr_ate_tune_config.yml

@@ -1,49 +1,33 @@
 Learner g,Learner m,Score,Approach,DGP,level,Tuned,Coverage,CI Length,Bias,Loss g,Loss m,repetition
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,False,0.89,0.07261917027364388,0.019235054197761097,1.8361787262583364,1.0404648655210367,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,True,0.89,0.07261917027364388,0.019235054197761097,1.8440047918328,1.0513028557786874,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,False,0.94,0.08653107850533552,0.019235054197761097,1.8361787262583364,1.0404648655210367,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,True,0.94,0.08653107850533552,0.019235054197761097,1.8440047918328,1.0513028557786874,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,False,0.6,0.0700152677964307,0.03198235111492452,1.8240864106139747,1.0151248226390528,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.9,True,0.6,0.0700152677964307,0.03198235111492452,1.8303518120277622,1.0253750664665986,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,False,0.69,0.08342833733069906,0.03198235111492452,1.8240864106139747,1.0151248226390528,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp2,0.95,True,0.69,0.08342833733069906,0.03198235111492452,1.8303518120277622,1.0253750664665986,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,False,0.0,0.1462163502282219,0.24567486215578818,2.2279933069499065,1.2505641228927882,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,True,0.0,0.1462163502282219,0.24567486215578818,2.222250935119112,1.2572923091327528,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,False,0.0,0.17422752742403425,0.24567486215578818,2.2279933069499065,1.2505641228927882,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,True,0.0,0.17422752742403425,0.24567486215578818,2.222250935119112,1.2572923091327528,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,False,0.355,0.10603858207543995,0.06489916106855874,1.8374578222083622,1.0735458838438718,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,True,0.355,0.10603858207543995,0.06489916106855874,1.842667271259383,1.0695053178941207,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,False,0.475,0.12635276381689156,0.06489916106855874,1.8374578222083622,1.0735458838438718,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,True,0.475,0.12635276381689156,0.06489916106855874,1.842667271259383,1.0695053178941207,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,False,0.915,0.09947368051992422,0.022277836350288207,1.8248827183224656,1.0385220092132057,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.9,True,0.915,0.09947368051992422,0.022277836350288207,1.8293718054414907,1.0238563379537278,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,False,0.965,0.11853020112801017,0.022277836350288207,1.8248827183224656,1.0385220092132057,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp2,0.95,True,0.965,0.11853020112801017,0.022277836350288207,1.8293718054414907,1.0238563379537278,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,False,0.14,0.1456697919504518,0.11380869099848887,2.229431291485762,1.4732977095693909,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,True,0.14,0.1456697919504518,0.11380869099848887,2.227865626320487,1.445298235347619,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,False,0.235,0.17357626306693333,0.11380869099848887,2.229431291485762,1.4732977095693909,200
-LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,True,0.235,0.17357626306693333,0.11380869099848887,2.227865626320487,1.445298235347619,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,False,0.75,0.08280385916225236,0.028686107344336492,1.789732218648727,1.5646363437982267,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,True,0.75,0.08280385916225236,0.028686107344336492,1.7768932970310254,1.5456576147179024,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,False,0.85,0.09866688383678877,0.028686107344336492,1.789732218648727,1.5646363437982267,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,True,0.85,0.09866688383678877,0.028686107344336492,1.7768932970310254,1.5456576147179024,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,False,0.705,0.08074359999671593,0.0318387032784245,1.6888041255737973,1.51878797250059,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.9,True,0.705,0.08074359999671593,0.0318387032784245,1.6401825056805919,1.4868034279511593,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,False,0.785,0.09621193362291841,0.0318387032784245,1.6888041255737973,1.51878797250059,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp2,0.95,True,0.785,0.09621193362291841,0.0318387032784245,1.6401825056805919,1.4868034279511593,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,False,0.0,0.13924492106829842,0.3399992035355032,2.6631585522839907,2.031731409601795,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,True,0.0,0.13924492106829842,0.3399992035355032,2.673299889854406,2.05218322369025,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,False,0.0,0.16592055721687587,0.3399992035355032,2.6631585522839907,2.031731409601795,200
-LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,True,0.0,0.16592055721687587,0.3399992035355032,2.673299889854406,2.05218322369025,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,False,0.87,0.06942711211214694,0.01876633539347498,1.1534314565687196,1.0238590256020452,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,True,0.87,0.06942711211214694,0.01876633539347498,1.1347790808297975,1.0040140440623913,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,False,0.94,0.08272750660654812,0.01876633539347498,1.1534314565687196,1.0238590256020452,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,True,0.94,0.08272750660654812,0.01876633539347498,1.1347790808297975,1.0040140440623913,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,False,0.695,0.062152399350612446,0.024051597450374388,1.1601279135717968,1.119614111898964,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.9,True,0.695,0.062152399350612446,0.024051597450374388,1.1166135931411847,1.0800167329397283,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,False,0.805,0.07405915169833223,0.024051597450374388,1.1601279135717968,1.119614111898964,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp2,0.95,True,0.805,0.07405915169833223,0.024051597450374388,1.1166135931411847,1.0800167329397283,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,False,0.0,0.07570291442887173,0.6331605054840328,2.4971967794668175,1.785265660424655,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,True,0.0,0.07570291442887173,0.6331605054840328,2.561461754242683,1.8300261872768113,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,False,0.0,0.09020558630514765,0.6331605054840328,2.4971967794668175,1.785265660424655,200
-LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,True,0.0,0.09020558630514765,0.6331605054840328,2.561461754242683,1.8300261872768113,200
+LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,False,0.85,0.03536060813686728,0.010137837243259291,1.759385900869758,0.9829393756223068,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.9,True,0.43,0.03545698050140429,0.019649649061510455,1.7952996538071346,1.0074865255650423,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,False,0.88,0.042134763412439845,0.010137837243259291,1.759385900869758,0.9829393756223068,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp1,0.95,True,0.6,0.04224959816764391,0.019649649061510455,1.7952996538071346,1.0074865255650423,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,False,0.0,0.06858490422345778,0.13496018593341227,1.9519737434421347,1.065153140737538,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.9,True,0.02,0.06394880355115651,0.06224369604076415,1.9303888300716567,1.0663837612950566,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,False,0.0,0.08172395400935707,0.13496018593341227,1.9519737434421347,1.065153140737538,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_general,dgp3,0.95,True,0.11,0.07619969932947211,0.06224369604076415,1.9303888300716567,1.0663837612950566,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,False,0.13,0.04499659790967875,0.037967155486427345,1.7597864638844198,1.000259350721019,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.9,True,0.05,0.04578777654320108,0.05361966770305959,1.7958356986277106,0.9953445685695448,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,False,0.19,0.053616753420942846,0.037967155486427345,1.7597864638844198,1.000259350721019,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp1,0.95,True,0.06,0.05455950091022274,0.05361966770305959,1.7958356986277106,0.9953445685695448,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,False,0.38,0.07033182725843065,0.0414062764743804,1.9511557372062653,1.292533000356088,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.9,True,0.75,0.07270339833567722,0.024516389764608012,1.9331219261429609,1.2700435982351912,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,False,0.5,0.08380554119511537,0.0414062764743804,1.9511557372062653,1.292533000356088,100
+LGBM Regr.,LGBM Regr.,partialling out,cre_normal,dgp3,0.95,True,0.85,0.08663144243156458,0.024516389764608012,1.9331219261429609,1.2700435982351912,100
+LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,False,0.9,0.04138210865225103,0.010006694800155022,1.6707591787769969,1.477648072202423,100
+LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.9,True,0.85,0.041322602449946635,0.011232911566422777,1.6567152207263192,1.467277534675784,100
+LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,False,0.94,0.04930982382490638,0.010006694800155022,1.6707591787769969,1.477648072202423,100
+LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp1,0.95,True,0.92,0.04923891781147008,0.011232911566422777,1.6567152207263192,1.467277534675784,100
+LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,False,0.0,0.06993795589874738,0.15347152370108225,1.9933735415428915,1.6344498070427307,100
+LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.9,True,0.0,0.06974766121306378,0.08962925447476038,1.9625374204301658,1.642912272067501,100
+LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,False,0.0,0.0833362145225945,0.15347152370108225,1.9933735415428915,1.6344498070427307,100
+LGBM Regr.,LGBM Regr.,partialling out,fd_exact,dgp3,0.95,True,0.0,0.08310946441895123,0.08962925447476038,1.9625374204301658,1.642912272067501,100
+LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,False,0.84,0.03477046525415805,0.00980255121555226,1.0936318545403032,0.9752601991470606,100
+LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.9,True,0.86,0.03476589854784296,0.009227874716762542,1.0883774496861216,0.9693908447489228,100
+LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,False,0.89,0.04143156479531641,0.00980255121555226,1.0936318545403032,0.9752601991470606,100
+LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp1,0.95,True,0.93,0.04142612322911326,0.009227874716762542,1.0883774496861216,0.9693908447489228,100
+LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,False,0.0,0.03627472064440453,0.6316795367533637,2.3058431372679435,1.6644837881054577,100
+LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.9,True,0.0,0.03648420793166297,0.6110007119820693,2.364071320754483,1.7088324600649871,100
+LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,False,0.0,0.043223995647596956,0.6316795367533637,2.3058431372679435,1.6644837881054577,100
+LGBM Regr.,LGBM Regr.,partialling out,wg_approx,dgp3,0.95,True,0.0,0.04347361514657118,0.6110007119820693,2.364071320754483,1.7088324600649871,100

results/plm/plpr_ate_tune_coverage.csv

@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (minutes),Python Version,Config File
-0.11.2.dev96,PLPRATETuningCoverageSimulation,2026-01-16 09:42,123.22425887982051,3.12.9,scripts/plm/plpr_ate_tune_config.yml
+0.11.2.dev96,PLPRATETuningCoverageSimulation,2026-01-16 16:54,76.14726469516754,3.12.9,scripts/plm/plpr_ate_tune_config.yml

results/plm/plpr_ate_tune_metadata.csv

@@ -9,7 +9,7 @@ simulation_parameters:
 dgp_parameters:
   theta: [0.5] # Treatment effect
   dim_x: [10] # Number of covariates
-  num_id: [250] # number of units
+  num_id: [1000] # number of units
   num_t: [10] # number of time periods
   DGP: ["dgp1", "dgp2", "dgp3"] # Data generating processes
 

scripts/plm/plpr_ate_config.yml

@@ -1,17 +1,17 @@
 # Simulation parameters for PLR ATE Coverage
 
 simulation_parameters:
-  repetitions: 200
+  repetitions: 100
   max_runtime: 19800 # 5.5 hours in seconds
   random_seed: 42
   n_jobs: -2
 
 dgp_parameters:
   theta: [0.5] # Treatment effect
   dim_x: [10] # Number of covariates
-  num_id: [250] # number of units
+  num_id: [1000] # number of units
   num_t: [10] # number of time periods
-  DGP: ["dgp1", "dgp2", "dgp3"] # Data generating processes
+  DGP: ["dgp1", "dgp3"] # Data generating processes
 
 # Define reusable learner configurations
 learner_definitions: