MetaCausal JSS replication: ACIC benchmark data and pre-computed ensemble results

Replication data and pre-computed results for the Journal of Statistical Software manuscript: MetaCausal: Ensemble Estimation of Causal Effects in Python. Contents: acic_data.zip — Raw ACIC benchmark input data (subset used by the manuscript): ACIC 2017 (32 DGPs × 3 seeds) and ACIC 2019 low-dimensional track (16 DGPs × 6 seeds). metacausal_results.zip — Pre-computed ensemble outputs. Extracting this archive and pointing the summarise / plot scripts at it reproduces all tables and figures without re-fitting the models. See the manuscript replication guide (Code/replicate_all.py) for usage instructions. Update. In the Section 5.2 CI-coverage experiment, EconML's CausalForestDML is now excluded from the bootstrap pool: its generalized random forest intermittently crashes (segmentation fault) under the repeated model refits the bootstrap performs, a latent and as-yet-unresolved upstream issue (EconML #470). The acic2019lo_nonparametric.csv and acic2019lo_subsample.csv files in metacausal_results.zip are therefore superseded; the corrected versions ship directly in the replication package. For reproduction, only the ACIC 2017 CATE parquet files in this archive are required (the Section 5.1 PEHE table); the acic2019lo_*.csv files here can be disregarded.《统计软件期刊》（Journal of Statistical Software）论文《MetaCausal：Python中因果效应的集成估计》的复现数据与预计算结果。 内容清单： acic_data.zip — ACIC基准输入原始数据集（本文所采用的子集）：ACIC 2017（32个数据生成过程（DGPs）×3个随机种子）以及ACIC 2019低维赛道（16个数据生成过程（DGPs）×6个随机种子）。 metacausal_results.zip — 预计算的集成模型输出结果。解压该归档文件并将路径指向汇总与绘图脚本，即可复现全部图表与表格，无需重新拟合模型。 使用说明请参阅论文复现指南（Code/replicate_all.py）。 更新说明：在第5.2节的置信区间覆盖率实验中，EconML的CausalForestDML现已从自助法池中移除：其广义随机森林在自助法执行的重复模型拟合过程中会间歇性崩溃（段错误），这是一个潜在且尚未得到解决的上游依赖问题（EconML #470）。因此metacausal_results.zip中的acic2019lo_nonparametric.csv与acic2019lo_subsample.csv文件已失效；修正后的版本已直接集成至本复现包中。如需复现实验，仅需使用本归档中的ACIC 2017 条件平均治疗效应（CATE）Parquet文件（对应第5.1节的异质治疗效应估计精度（PEHE）表格），本归档中的acic2019lo_*.csv文件可忽略不计。