We lifted 14 functions out of this paper's own repositories and ran 9 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jeffnclark/trace | canonical | 9 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| change_categorical | Ran | jeffnclark/trace/helpers/funcs_icu_study.py code served (permissive licence) · get_code("0ce50857b146ff6e") |
| cos_sim | Ran | jeffnclark/trace/helpers/funcs.py code served (permissive licence) · get_code("bff9357caec21792") |
| n_sphere | Ran | jeffnclark/trace/helpers/funcs.py code served (permissive licence) · get_code("9c7a8779b32dbd02") |
| nan_post_processing_data | Ran | jeffnclark/trace/helpers/funcs_icu_study.py code served (permissive licence) · get_code("8dcf34339f3961ce") |
| obtain_stay_id_individuals | Ran | jeffnclark/trace/ICU_TraCE_scores.py code served (permissive licence) · get_code("51e1fcbf1e618076") |
| plot_dataset | Ran | jeffnclark/trace/helpers/plotters.py code served (permissive licence) · get_code("f6e80cda9b180e6d") |
| plot_decision_boundary | Ran | jeffnclark/trace/helpers/plotters.py code served (permissive licence) · get_code("bb274893d5e95c1a") |
| plot_density | Ran | jeffnclark/trace/helpers/plotters.py code served (permissive licence) · get_code("6377a0fb1f97b336") |
| vec | Ran | jeffnclark/trace/helpers/funcs.py code served (permissive licence) · get_code("ffa2aada7cc61d14") |
| cf_generator | Not yet run | jeffnclark/trace/helpers/funcs_icu_study.py code served (permissive licence) · get_code("ecef3ab232914eb3") |
| convert_nc_to_csv | Not yet run | jeffnclark/trace/helpers/funcs_ssp_study.py code served (permissive licence) · get_code("e31cd68943331ac1") |
| generate_dice_cf_global | Not yet run | jeffnclark/trace/ICU_TraCE_scores.py code served (permissive licence) · get_code("9d32e662b0e911b9") |
| merge_exp_versions | Not yet run | jeffnclark/trace/helpers/funcs_ssp_study.py code served (permissive licence) · get_code("53fb628bd87a789a") |
| train_classifier | Not yet run | jeffnclark/trace/ICU_TraCE_scores.py code served (permissive licence) · get_code("2080bccdc34c542c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box classifier. In this paper, we propose to extend the use of counterfactuals to evaluate progress in sequential decision making tasks. To this end, we introduce a model-agnostic modular framework, TraCE (Trajectory Counterfactual Explanation) scores, which is able to distill and condense progress in highly complex scenarios into a single value. We demonstrate TraCE's utility across domains by showcasing its main properties in two case studies spanning healthcare and climate change.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2309.15965")
get_code_for_paper("2309.15965")
have("2309.15965")
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