We lifted 13 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| clinicalml/overlap-code | canonical | 7 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| advCube | Ran | clinicalml/overlap-code/exps/supp-synthetic/data.py code served (permissive licence) · get_code("f174f1c301e4fbb3") |
| beam_search | Ran | clinicalml/overlap-code/overrule/BCS/beam_search.py code served (permissive licence) · get_code("02dc109ffee84309") |
| calc_coverage | Ran | clinicalml/overlap-code/exps/supp-synthetic/synth_utils.py code served (permissive licence) · get_code("7e8a806815a34c28") |
| compliance | Ran | clinicalml/overlap-code/overrule/visualization.py code served (permissive licence) · get_code("7618a1787899f3ab") |
| get_feat | Ran | clinicalml/overlap-code/exps/supp-synthetic/data.py code served (permissive licence) · get_code("9268406fb73cb2bc") |
| sampleUnif | Ran | clinicalml/overlap-code/overrule/utils.py code served (permissive licence) · get_code("ca5f6d83d0f63806") |
| sample_reference | Ran | clinicalml/overlap-code/overrule/utils.py code served (permissive licence) · get_code("ceb12f30ebcd5ac8") |
| beam_search_K1 | Not yet run | clinicalml/overlap-code/overrule/BCS/beam_search.py code served (permissive licence) · get_code("9a4547b9839a7716") |
| beam_search_no_dup | Not yet run | clinicalml/overlap-code/overrule/BCS/beam_search.py code served (permissive licence) · get_code("82cc61fbbdb18a91") |
| compute_metrics | Not yet run | clinicalml/overlap-code/overrule/utils.py code served (permissive licence) · get_code("ff46735c2f67ac5e") |
| eval_confusion_matrix | Not yet run | clinicalml/overlap-code/exps/supp-synthetic/synth_utils.py code served (permissive licence) · get_code("510723081c5e1bbb") |
| print_surgery_rules | Not yet run | clinicalml/overlap-code/overrule/visualization.py code served (permissive licence) · get_code("925fab886dce2d30") |
| print_uti_rules | Not yet run | clinicalml/overlap-code/overrule/visualization.py code served (permissive licence) · get_code("5f9c40e76f8e9df3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate the effect of intervention changes on that subgroup without further assumptions. When overlap does not hold globally, characterizing local regions of overlap can inform the relevance of causal conclusions for new subjects, and can help guide additional data collection. To have impact, these descriptions must be interpretable for downstream users who are not machine learning experts, such as policy makers. We formalize overlap estimation as a problem of finding minimum volume sets subject to coverage constraints and reduce this problem to binary classification with Boolean rule classifiers. We then generalize this method to estimate overlap in off-policy policy evaluation. In several real-world applications, we demonstrate that these rules have comparable accuracy to black-box estimators and provide intuitive and informative explanations that can inform policy making.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1907.04138")
get_code_for_paper("1907.04138")
have("1907.04138")
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