We lifted 6 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| larsvanderlaan/causalcalibration | canonical | 3 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| as_matrix_rows | Ran | larsvanderlaan/causalcalibration/python/src/causal_calibration/_utils.py code served (permissive licence) · get_code("db4c697838c9fc4c") |
| as_optional_vector | Ran | larsvanderlaan/causalcalibration/python/src/causal_calibration/_utils.py code served (permissive licence) · get_code("6bc66c66af7227cb") |
| as_vector | Ran | larsvanderlaan/causalcalibration/python/src/causal_calibration/_utils.py code served (permissive licence) · get_code("a80d71d2e6bda0ae") |
| fit_isotonic | Not yet run | larsvanderlaan/causalcalibration/python/src/causal_calibration/_algorithms.py code served (permissive licence) · get_code("9c988b3da27baf30") |
| fit_linear | Not yet run | larsvanderlaan/causalcalibration/python/src/causal_calibration/_algorithms.py code served (permissive licence) · get_code("db7309191da349b0") |
| fit_monotone_spline | Not yet run | larsvanderlaan/causalcalibration/python/src/causal_calibration/_algorithms.py code served (permissive licence) · get_code("0372e92243f5b812") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a data-efficient variant of calibration that eliminates the need for hold-out calibration sets. Cross-calibration leverages cross-fitted predictors and generates a single calibrated predictor using all available data. Under weak conditions that do not assume monotonicity, we establish that both causal isotonic calibration and cross-calibration achieve fast doubly-robust calibration rates, as long as either the propensity score or outcome regression is estimated accurately in a suitable sense. The proposed causal isotonic calibrator can be wrapped around any black-box learning algorithm, providing robust and distribution-free calibration guarantees while preserving predictive performance.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.14011")
get_code_for_paper("2302.14011")
have("2302.14011")
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