{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/causal-isotonic-calibration-for-heterogeneous","title":"Causal isotonic calibration for heterogeneous treatment effects","arxiv_id":"2302.14011","date":"2023-02-27","proceeding":null,"authors":["Lars van der Laan","Ernesto Ulloa-Pérez","Marco Carone","Alex Luedtke"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2302.14011v2","url_pdf":"https://arxiv.org/pdf/2302.14011v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"causal-isotonic-calibration-for-heterogeneous","repo_url":"https://github.com/larsvanderlaan/causalcalibration","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2302.14011","atlas_url":"https://app.syntology.ai/?focus=2302.14011","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.14011"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/larsvanderlaan/causalcalibration","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":3,"unverified":3},"by_repo_kind":{"official":{"samples":6,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"db4c697838c9fc4c","entry":"as_matrix_rows","repo":"larsvanderlaan/causalcalibration","repo_kind":"official","path":"python/src/causal_calibration/_utils.py","file_url":"https://github.com/larsvanderlaan/causalcalibration/blob/HEAD/python/src/causal_calibration/_utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"db4c697838c9fc4c"}},{"code_sha256_prefix":"6bc66c66af7227cb","entry":"as_optional_vector","repo":"larsvanderlaan/causalcalibration","repo_kind":"official","path":"python/src/causal_calibration/_utils.py","file_url":"https://github.com/larsvanderlaan/causalcalibration/blob/HEAD/python/src/causal_calibration/_utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"6bc66c66af7227cb"}},{"code_sha256_prefix":"a80d71d2e6bda0ae","entry":"as_vector","repo":"larsvanderlaan/causalcalibration","repo_kind":"official","path":"python/src/causal_calibration/_utils.py","file_url":"https://github.com/larsvanderlaan/causalcalibration/blob/HEAD/python/src/causal_calibration/_utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a80d71d2e6bda0ae"}},{"code_sha256_prefix":"9c988b3da27baf30","entry":"fit_isotonic","repo":"larsvanderlaan/causalcalibration","repo_kind":"official","path":"python/src/causal_calibration/_algorithms.py","file_url":"https://github.com/larsvanderlaan/causalcalibration/blob/HEAD/python/src/causal_calibration/_algorithms.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"9c988b3da27baf30"}},{"code_sha256_prefix":"db7309191da349b0","entry":"fit_linear","repo":"larsvanderlaan/causalcalibration","repo_kind":"official","path":"python/src/causal_calibration/_algorithms.py","file_url":"https://github.com/larsvanderlaan/causalcalibration/blob/HEAD/python/src/causal_calibration/_algorithms.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"db7309191da349b0"}},{"code_sha256_prefix":"0372e92243f5b812","entry":"fit_monotone_spline","repo":"larsvanderlaan/causalcalibration","repo_kind":"official","path":"python/src/causal_calibration/_algorithms.py","file_url":"https://github.com/larsvanderlaan/causalcalibration/blob/HEAD/python/src/causal_calibration/_algorithms.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0372e92243f5b812"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}