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Srishti280992
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causal-effect-identifiability-repro-bundle

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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 3 new columns ({'empirical_status', 'theory_identifiable', 'pattern'})

This happened while the csv dataset builder was generating data using

hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle/outputs/broad_full/claim2_rgpo_characterization.csv (at revision ed49c7ea01528a70e4bb0149a074482375d680ef), ['hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/broad_full/claim1_cumulant_recovery.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/broad_full/claim2_rgpo_characterization.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/broad_full/claim3_feasible_scan.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/broad_full/claim3_gaussian_feasible_cardinality.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/condition3_finite_cardinality.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/condition4_continuum_witnesses.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/finite_sample_mechanism_replay.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/identifiable_population_certificates.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/official_code_replay.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/rgpo_truth_table_54.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/local_full/claim1_cumulant_recovery.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/local_full/claim2_rgpo_characterization.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/local_full/claim3_feasible_scan.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/local_full/claim3_gaussian_feasible_cardinality.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              claim: string
              trial: int64
              latent_noise: string
              treatment_noise: string
              outcome_noise: string
              pattern: string
              theory_identifiable: bool
              empirical_status: string
              b: double
              gamma: double
              true_alpha: double
              alpha_hat: double
              abs_error: double
              success: bool
              method: string
              k40: double
              k31: double
              k22: double
              k13: double
              var_x: double
              var_y: double
              cov_xy: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2812
              to
              {'claim': Value('string'), 'trial': Value('int64'), 'latent_noise': Value('string'), 'treatment_noise': Value('string'), 'outcome_noise': Value('string'), 'b': Value('float64'), 'gamma': Value('float64'), 'true_alpha': Value('float64'), 'alpha_hat': Value('float64'), 'abs_error': Value('float64'), 'success': Value('bool'), 'method': Value('string'), 'k40': Value('float64'), 'k31': Value('float64'), 'k22': Value('float64'), 'k13': Value('float64'), 'var_x': Value('float64'), 'var_y': Value('float64'), 'cov_xy': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 3 new columns ({'empirical_status', 'theory_identifiable', 'pattern'})
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle/outputs/broad_full/claim2_rgpo_characterization.csv (at revision ed49c7ea01528a70e4bb0149a074482375d680ef), ['hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/broad_full/claim1_cumulant_recovery.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/broad_full/claim2_rgpo_characterization.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/broad_full/claim3_feasible_scan.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/broad_full/claim3_gaussian_feasible_cardinality.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/condition3_finite_cardinality.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/condition4_continuum_witnesses.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/finite_sample_mechanism_replay.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/identifiable_population_certificates.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/official_code_replay.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/exact_audit/rgpo_truth_table_54.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/local_full/claim1_cumulant_recovery.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/local_full/claim2_rgpo_characterization.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/local_full/claim3_feasible_scan.csv', 'hf://datasets/Srishti280992/causal-effect-identifiability-repro-bundle@ed49c7ea01528a70e4bb0149a074482375d680ef/outputs/local_full/claim3_gaussian_feasible_cardinality.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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claim
string
trial
int64
latent_noise
string
treatment_noise
string
outcome_noise
string
b
float64
gamma
float64
true_alpha
float64
alpha_hat
float64
abs_error
float64
success
bool
method
string
k40
float64
k31
float64
k22
float64
k13
float64
var_x
float64
var_y
float64
cov_xy
float64
claim1
0
gaussian
exponential
gaussian
1.093527
0.372112
1.114267
1.115104
0.000838
true
cum31_over_cum40_treatment_source
5.874184
6.550328
7.307365
8.156862
2.194551
4.779534
2.855039
claim1
1
gaussian
exponential
gaussian
0.557368
0.98186
1.13811
1.143054
0.004944
true
cum31_over_cum40_treatment_source
6.087615
6.958472
7.960898
9.119031
1.308877
4.897985
2.032442
claim1
2
gaussian
exponential
gaussian
1.192371
0.393529
0.88069
0.890286
0.009596
true
cum31_over_cum40_treatment_source
5.993544
5.335969
4.749428
4.2258
2.423296
3.866175
2.60595
claim1
0
gaussian
laplace
gaussian
0.601749
0.630176
1.489403
1.484727
0.004676
true
cum31_over_cum40_treatment_source
3.057967
4.540247
6.742333
10.020345
1.364391
5.552903
2.411966
claim1
1
gaussian
laplace
gaussian
1.025298
0.764681
1.457037
1.455078
0.001959
true
cum31_over_cum40_treatment_source
2.953254
4.297215
6.258307
9.126403
2.04849
8.209513
3.766161
claim1
2
gaussian
laplace
gaussian
0.97333
0.619317
0.998973
1.004813
0.00584
true
cum31_over_cum40_treatment_source
3.041352
3.055989
3.076977
3.11159
1.947541
4.53428
2.549937
claim1
0
gaussian
uniform
gaussian
0.837788
0.92004
0.897793
0.896469
0.001325
true
cum31_over_cum40_treatment_source
-1.183459
-1.060934
-0.95946
-0.893886
1.70026
4.602163
2.29786
claim1
1
gaussian
uniform
gaussian
0.746522
0.403748
1.617405
1.618861
0.001455
true
cum31_over_cum40_treatment_source
-1.186499
-1.920776
-3.113678
-5.066253
1.553265
6.195203
2.812011
claim1
2
gaussian
uniform
gaussian
0.705666
0.44275
1.427709
1.432744
0.005035
true
cum31_over_cum40_treatment_source
-1.202884
-1.723425
-2.471027
-3.547877
1.496345
5.137601
2.448873
claim1
0
gaussian
gaussian
gaussian
0.961667
0.476863
0.796175
1.033518
0.237343
false
covariance_regression_baseline_unidentified
0.012432
0.021964
0.035495
0.044215
1.921168
3.170001
1.985561
claim1
1
gaussian
gaussian
gaussian
0.718747
0.681341
1.466413
1.790097
0.323683
false
covariance_regression_baseline_unidentified
-0.024607
-0.044582
-0.085108
-0.172848
1.514054
6.15769
2.710303
claim1
2
gaussian
gaussian
gaussian
0.898101
0.672377
1.351982
1.684455
0.332473
false
covariance_regression_baseline_unidentified
0.011561
0.016828
0.02062
0.014719
1.807905
6.380373
3.045335
claim2
0
gaussian
gaussian
gaussian
0.692124
0.652431
1.320949
null
null
true
all_gaussian_covariance_only
0.001529
0.014857
0.049576
0.12757
1.480807
5.199499
2.407355
claim2
1
gaussian
gaussian
gaussian
0.790128
1.10939
0.989509
null
null
true
all_gaussian_covariance_only
0.036209
0.054244
0.077351
0.101496
1.621566
5.553312
2.481232
claim2
2
gaussian
gaussian
gaussian
0.904432
1.035729
1.325207
null
null
true
all_gaussian_covariance_only
-0.015338
-0.024432
-0.031074
-0.014788
1.820553
7.759596
3.351576
claim2
0
gaussian
gaussian
laplace
0.921881
0.823913
1.015749
null
null
true
all_gaussian_covariance_only
-0.017127
-0.029733
-0.048149
-0.070131
1.85089
5.13595
2.64115
claim2
1
gaussian
gaussian
laplace
0.897614
0.895156
0.824052
null
null
true
all_gaussian_covariance_only
-0.003888
-0.004793
0.001257
0.031505
1.804249
4.346531
2.288849
claim2
2
gaussian
gaussian
laplace
1.131932
0.721199
1.341214
null
null
true
all_gaussian_covariance_only
-0.023413
-0.043271
-0.063226
-0.077044
2.284731
7.819843
3.880902
claim2
0
gaussian
gaussian
exponential
0.606376
0.928927
1.626544
null
null
true
all_gaussian_covariance_only
-0.022585
-0.029883
-0.03119
-0.024217
1.364759
7.290135
2.778215
claim2
1
gaussian
gaussian
exponential
0.60653
0.703249
1.610497
null
null
true
all_gaussian_covariance_only
-0.010442
-0.027157
-0.06507
-0.170951
1.368216
6.423441
2.631373
claim2
2
gaussian
gaussian
exponential
0.660037
1.033044
1.150217
null
null
true
all_gaussian_covariance_only
0.011386
0.01214
0.020334
0.024587
1.431519
5.529721
2.326061
claim2
0
gaussian
gaussian
uniform
0.483347
1.083297
0.79211
null
null
true
all_gaussian_covariance_only
0.005177
0.002967
0.006343
0.027888
1.235032
3.773083
1.501033
claim2
1
gaussian
gaussian
uniform
0.570765
0.539464
0.86844
null
null
true
all_gaussian_covariance_only
-0.006056
0.000935
0.010823
0.020932
1.326367
2.822943
1.458471
claim2
2
gaussian
gaussian
uniform
1.226625
0.374467
0.964522
null
null
true
all_gaussian_covariance_only
0.015264
0.026454
0.032363
0.033918
2.507648
4.350209
2.873835
claim2
0
gaussian
laplace
gaussian
0.624917
0.588073
1.327392
1.323801
0.003591
true
cum31_over_cum40_treatment_source
3.052102
4.040375
5.353159
7.0943
1.391091
4.781592
2.216568
claim2
1
gaussian
laplace
gaussian
0.945648
1.099354
1.230758
1.233212
0.002455
true
cum31_over_cum40_treatment_source
3.087668
3.807749
4.692853
5.793801
1.890389
7.630355
3.365837
claim2
2
gaussian
laplace
gaussian
0.859944
1.110738
1.512463
1.520189
0.007726
true
cum31_over_cum40_treatment_source
3.038765
4.619497
7.02866
10.701434
1.73578
9.084107
3.578631
claim2
0
gaussian
laplace
laplace
1.103324
0.935331
1.258248
1.249399
0.008848
true
cum31_over_cum40_treatment_source
2.951738
3.6879
4.608459
5.77108
2.217858
7.990009
3.824021
claim2
1
gaussian
laplace
laplace
0.993821
0.727563
1.216867
1.213335
0.003532
true
cum31_over_cum40_treatment_source
2.967844
3.600988
4.33776
5.163335
1.990538
6.242912
3.148027
claim2
2
gaussian
laplace
laplace
0.992911
0.482753
1.143451
1.129255
0.014196
true
cum31_over_cum40_treatment_source
2.997071
3.384456
3.817386
4.29422
1.980151
4.910771
2.740329
claim2
0
gaussian
laplace
exponential
0.69346
0.885911
0.926037
0.924221
0.001816
true
cum31_over_cum40_treatment_source
2.921256
2.699886
2.502134
2.348331
1.478192
4.187385
1.98161
claim2
1
gaussian
laplace
exponential
1.059228
0.540426
1.355031
1.354277
0.000754
true
cum31_over_cum40_treatment_source
2.985637
4.04338
5.461552
7.353677
2.11796
6.737023
3.444089
claim2
2
gaussian
laplace
exponential
0.948026
0.673429
1.03629
1.03853
0.002241
true
cum31_over_cum40_treatment_source
2.913667
3.025932
3.15239
3.26069
1.896653
4.806614
2.601456
claim2
0
gaussian
laplace
uniform
0.710671
0.899888
1.034776
1.03234
0.002436
true
cum31_over_cum40_treatment_source
2.982492
3.078946
3.171207
3.239128
1.504728
4.738586
2.19615
claim2
1
gaussian
laplace
uniform
1.053937
0.810908
1.095327
1.096418
0.001091
true
cum31_over_cum40_treatment_source
3.06248
3.357757
3.695332
4.092827
2.110379
6.05834
3.164955
claim2
2
gaussian
laplace
uniform
0.941506
1.075478
1.137478
1.134893
0.002585
true
cum31_over_cum40_treatment_source
2.85075
3.235295
3.672247
4.160717
1.887924
6.920867
3.165064
claim2
0
gaussian
exponential
gaussian
0.769945
0.61038
1.631464
1.638782
0.007318
true
cum31_over_cum40_treatment_source
6.005825
9.842236
16.140593
26.486705
1.594999
7.157628
3.074158
claim2
1
gaussian
exponential
gaussian
0.999249
0.927386
1.47219
1.479282
0.007091
true
cum31_over_cum40_treatment_source
6.238621
9.228676
13.649386
20.178324
1.997492
8.911756
3.865218
claim2
2
gaussian
exponential
gaussian
0.787624
0.841135
0.954049
0.952703
0.001346
true
cum31_over_cum40_treatment_source
6.058587
5.772033
5.480126
5.180344
1.619739
4.437681
2.20507
claim2
0
gaussian
exponential
laplace
0.975559
0.57396
0.717019
0.719771
0.002752
true
cum31_over_cum40_treatment_source
5.903571
4.24922
3.070989
2.250802
1.952096
3.131822
1.957924
claim2
1
gaussian
exponential
laplace
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End of preview.

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Reproduction Bundle

Paper: Causal Effect Identifiability in the Presence of Latent Confounders Without Auxiliary Variables

OpenReview: https://openreview.net/forum?id=8ewf5I4shW

Logbook Space: https://huggingface.co/spaces/Srishti280992/repro-causal-effect-identifiability-in-the-presence-of-latent-confounders-without-auxiliary-vari

This bundle contains the source-locked audit evidence used in the Trackio logbook. It verifies the three challenge claims with exact population certificates, official-code finite-sample replay, an exhaustive RGPO Gaussianity truth table, cardinality witnesses, and destructive controls.

Source Lock

Claims Checked

  1. Gaussianity dependence: 243 exact population cells across RGPO Conditions 1 and 2 recover causal effects to machine precision, and 32 official-code runs at N=500,000 beat zero-effect baselines.
  2. Complete RGPO characterization: 54 Gaussianity assignments are enumerated, every row receives exactly one of Conditions 1-4, and all four conditions are reached.
  3. Cardinality quantification: Condition 3 yields exactly m + 1 observationally identical alternatives across 7 latent counts; Condition 4 constructs 10,001 continuum witnesses with an analytic interval certificate.

Files

  • scripts/exact_rgpo_audit.py: exact audit and replay driver.
  • outputs/exact_audit/evidence.json: full machine-readable evidence summary.
  • outputs/exact_audit/SCIENCE_GATES.json: pass/fail gates for the three claims.
  • outputs/exact_audit/identifiable_population_certificates.csv: Claim 1 population certificates.
  • outputs/exact_audit/official_code_replay.csv: official implementation finite-sample runs.
  • outputs/exact_audit/rgpo_truth_table_54.csv: Claim 2 RGPO truth table.
  • outputs/exact_audit/condition3_finite_cardinality.csv: Claim 3 finite-cardinality certificates.
  • outputs/exact_audit/condition4_continuum_witnesses.csv: Claim 3 continuum witnesses.
  • outputs/exact_audit/finite_sample_mechanism_replay.csv: supplementary convergence checks.
  • poster/: poster source and embed used in the logbook.

Local Rerun 

python -m venv .venv
. .venv/bin/activate
pip install numpy pandas scipy
python scripts/exact_rgpo_audit.py \
  --outdir outputs/exact_audit \
  --source-dir source/ICML2026-identifiability \
  --paper-pdf paper/openreview_8ewf5I4shW.pdf \
  --n-official 500000

The script writes the same JSON and CSV evidence files under outputs/exact_audit/.
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