Update results from script: scripts/irm/cvar_coverage.py

Author: github-actions

results/irm/cvar_coverage_metadata.csv

@@ -1,2 +1,2 @@
 DoubleML Version,Script,Date,Total Runtime (seconds),Python Version
-0.9.0,cvar_coverage.py,2024-09-09 11:59:26,16546.96124267578,3.12.5
+0.10.dev0,cvar_coverage.py,2025-01-08 16:49:01,16412.1168384552,3.12.8

results/irm/cvar_coverage_pq0.csv

@@ -1,9 +1,9 @@
 Learner g,Learner m,level,Coverage,CI Length,Bias,repetition
-LGBM,LGBM,0.9,0.8876923076923078,0.5699689541680535,0.13934148600639645,100
-LGBM,LGBM,0.95,0.9461538461538461,0.6791598985897508,0.13934148600639645,100
-LGBM,Logistic Regression,0.9,0.8192307692307692,0.4060118886357097,0.1185635852924032,100
-LGBM,Logistic Regression,0.95,0.89,0.48379300503226824,0.1185635852924032,100
-Linear,LGBM,0.9,0.77,0.5801661718639771,0.17503081399943532,100
-Linear,LGBM,0.95,0.8607692307692308,0.691310632915921,0.17503081399943532,100
-Linear,Logistic Regression,0.9,0.6892307692307692,0.4294636844188598,0.15444826651283644,100
-Linear,Logistic Regression,0.95,0.7776923076923077,0.5117375432906374,0.15444826651283644,100
+LGBM,LGBM,0.9,0.8884615384615384,0.5699689541680535,0.13934954721406115,100
+LGBM,LGBM,0.95,0.9461538461538461,0.6791598985897508,0.13934954721406115,100
+LGBM,Logistic Regression,0.9,0.8192307692307692,0.4060217020758364,0.11848308187809455,100
+LGBM,Logistic Regression,0.95,0.8892307692307692,0.48380469846741503,0.11848308187809455,100
+Linear,LGBM,0.9,0.7692307692307692,0.5801661718639771,0.1751137266106609,100
+Linear,LGBM,0.95,0.8615384615384616,0.691310632915921,0.1751137266106609,100
+Linear,Logistic Regression,0.9,0.6892307692307692,0.42946981193983413,0.1545041280672664,100
+Linear,Logistic Regression,0.95,0.7776923076923077,0.5117448446822186,0.1545041280672664,100

results/irm/cvar_coverage_pq1.csv

@@ -1,9 +1,9 @@
 Learner g,Learner m,level,Coverage,CI Length,Bias,repetition
-LGBM,LGBM,0.9,0.9323076923076923,0.1908311409902022,0.04347081995558675,100
-LGBM,LGBM,0.95,0.9776923076923077,0.22738932956769187,0.04347081995558675,100
-LGBM,Logistic Regression,0.9,0.9146153846153847,0.1776482729176802,0.04405662937860094,100
-LGBM,Logistic Regression,0.95,0.9646153846153847,0.21168097338831957,0.04405662937860094,100
-Linear,LGBM,0.9,0.9307692307692308,0.21584587388672186,0.04750929738761366,100
-Linear,LGBM,0.95,0.9761538461538461,0.25719622226423833,0.04750929738761366,100
-Linear,Logistic Regression,0.9,0.8853846153846153,0.1934225485324801,0.04921876802543021,100
-Linear,Logistic Regression,0.95,0.943076923076923,0.23047718210904136,0.04921876802543021,100
+LGBM,LGBM,0.9,0.9323076923076923,0.1908311409902022,0.04358145932430658,100
+LGBM,LGBM,0.95,0.9776923076923077,0.22738932956769187,0.04358145932430658,100
+LGBM,Logistic Regression,0.9,0.9130769230769231,0.17764550716816235,0.04414608757632295,100
+LGBM,Logistic Regression,0.95,0.963076923076923,0.21167767779450106,0.04414608757632295,100
+Linear,LGBM,0.9,0.9292307692307692,0.21584587388672186,0.047457925834839765,100
+Linear,LGBM,0.95,0.9769230769230769,0.25719622226423833,0.047457925834839765,100
+Linear,Logistic Regression,0.9,0.8823076923076922,0.19342205011642188,0.0494625033437771,100
+Linear,Logistic Regression,0.95,0.9415384615384617,0.2304765882096771,0.0494625033437771,100

results/irm/cvar_coverage_qte.csv

@@ -1,9 +1,9 @@
 Learner g,Learner m,level,Coverage,CI Length,Bias,Uniform Coverage,Uniform CI Length,repetition
-LGBM,LGBM,0.9,0.9007692307692308,0.5818966962059838,0.14385931104084665,0.88,0.7055777033989438,100
-LGBM,LGBM,0.95,0.9492307692307692,0.693372679853793,0.14385931104084665,0.95,0.8113769406399532,100
-LGBM,Logistic Regression,0.9,0.8092307692307692,0.4171534550934474,0.12274374044729776,0.78,0.5062270696539016,100
-LGBM,Logistic Regression,0.95,0.87,0.49706900031277096,0.12274374044729776,0.86,0.5834562695237335,100
-Linear,LGBM,0.9,0.8007692307692308,0.6065907561805581,0.179698169022633,0.8,0.7182897195756133,100
-Linear,LGBM,0.95,0.8653846153846153,0.7227974671960807,0.179698169022633,0.85,0.8301176550726784,100
-Linear,Logistic Regression,0.9,0.7092307692307692,0.454167116768984,0.15248058466727346,0.69,0.5341820696542956,100
-Linear,Logistic Regression,0.95,0.8123076923076923,0.5411734985072126,0.15248058466727346,0.8,0.6185623415096295,100
+LGBM,LGBM,0.9,0.9038461538461539,0.5818966962059838,0.1435944755709475,0.88,0.7055777033989438,100
+LGBM,LGBM,0.95,0.9492307692307692,0.693372679853793,0.1435944755709475,0.94,0.8113769406399532,100
+LGBM,Logistic Regression,0.9,0.81,0.41716340917968886,0.12258144778601979,0.78,0.5062698827872832,100
+LGBM,Logistic Regression,0.95,0.8738461538461538,0.49708086133810014,0.12258144778601979,0.86,0.5835143322993298,100
+Linear,LGBM,0.9,0.8023076923076923,0.6065907561805581,0.1790914579109802,0.8,0.7182897195756133,100
+Linear,LGBM,0.95,0.8653846153846153,0.7227974671960807,0.1790914579109802,0.85,0.8301176550726784,100
+Linear,Logistic Regression,0.9,0.713076923076923,0.4541725983633358,0.15175636482362434,0.71,0.5341780882458428,100
+Linear,Logistic Regression,0.95,0.8146153846153846,0.541180030229751,0.15175636482362434,0.8,0.6185746229300051,100