How to select predictive models for decision making or causal inference? Experiments data

This is the full result data for the experiments of the paper : Doutreligne, M., & Varoquaux, G. (2023). How to select predictive models for decision making or causal inference?, https://hal.science/hal-03946902. The code repository is : https://github.com/soda-inria/caussim/tree/main The files in this dataset are the one for the most computationnally costly experiments. There is one folder for each of the four datasets used in the paper. Then, one folder for each of the experimental setup. The files required for the main figure (Fig.3) of the paper are the one labelled #fig3 in the following descriptions. Details on the files : .├── acic_2016_save│ ├── acic_2016__nuisance_non_linear__candidates_hist_gradient_boosting__dgp_1-77__rs_1-5│ │ └── run_logs.csv: results for the experiment with non linear models for both the nuisances and the candidates│ ├── acic_2016__nuisance_non_linear__candidates_ridge__dgp_1-77__rs_1-10│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates│ └── acic_2016__stacked_regressor__dgp_1-77__seed_1-10│ └── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and non linear models for the candidates #fig3├── acic_2018_save│ └── acic_2018__nuisance_non_linear__candidates_hist_gradient_boosting__first_uid_432│ └── run_logs.csv results for the experiment with stacked models (linear and non linear) for the nuisances models and non linear models for the candidates #fig3├── caussim_save│ ├── caussim__linear_regressor__test_size_5000__n_datasets_1000│ │ ├── run_logs.csv: results for the experiment with stacked models for the nuisances models and linear models for the candidates │ │ └── simu.yaml: configuration file of the experiment│ ├── caussim__nuisance_non_linear__candidates_ridge__overlap_01-247_join_nuisance_train_set│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates, joined sets for the nuisances and the candidates│ ├── caussim__nuisance_non_linear__candidates_ridge__overlap_01-247_separated_nuisance_train_set│ │ └── run_logs.csv: results for the experiment with non linear models for the nuisances and linear models for the candidates, separated sets for the nuisances and the candidates│ └── caussim__stacked_regressor__test_size_5000__n_datasets_1000│ ├── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and linear models for the candidates #fig3│ └── simu.yaml: configuration file of the experiment└── twins_save └── twins__stacked_regressor__rs_1-10__overlap_0.1-3 └── run_logs.csv: results for the experiment with stacked models (linear and non linear) for the nuisances and non linear models for the candidates #fig3