{"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/longitudinal-targeted-minimum-loss-based","title":"Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer","arxiv_id":"2404.04399","date":"2024-04-05","proceeding":null,"authors":["Toru Shirakawa","Yi Li","Yulun Wu","Sky Qiu","YuXuan Li","Mingduo Zhao","Hiroyasu Iso","Mark van der Laan"],"abstract":"We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies in longitudinal problem settings. Our approach utilizes a transformer architecture with heterogeneous type embedding trained using temporal-difference learning. After obtaining an initial estimate using the transformer, following the targeted minimum loss-based likelihood estimation (TMLE) framework, we statistically corrected for the bias commonly associated with machine learning algorithms. Furthermore, our method also facilitates statistical inference by enabling the provision of 95% confidence intervals grounded in asymptotic statistical theory. Simulation results demonstrate our method's superior performance over existing approaches, particularly in complex, long time-horizon scenarios. It remains effective in small-sample, short-duration contexts, matching the performance of asymptotically efficient estimators. To demonstrate our method in practice, we applied our method to estimate counterfactual mean outcomes for standard versus intensive blood pressure management strategies in a real-world cardiovascular epidemiology cohort study.","url_abs":"https://arxiv.org/abs/2404.04399v2","url_pdf":"https://arxiv.org/pdf/2404.04399v2.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":"longitudinal-targeted-minimum-loss-based","repo_url":"https://github.com/shirakawatoru/dltmle-icml-2024","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"epidemiology","task_name":"Epidemiology"},{"task_slug":"management","task_name":"Management"},{"task_slug":null,"task_name":"counterfactual"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2404.04399","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.04399"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/shirakawatoru/dltmle-icml-2024","reach":null}],"summary":{"ran":1,"ran_honours":1,"unverified":1},"by_repo_kind":{"listed":{"samples":3,"ran":2,"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":3,"samples":[{"code_sha256_prefix":"ef291f17a8c339a4","entry":"SinusoidalEncoder","repo":"shirakawatoru/dltmle-icml-2024","repo_kind":"listed","path":"src/model/LAY/dltmle.py","file_url":"https://github.com/shirakawatoru/dltmle-icml-2024/blob/HEAD/src/model/LAY/dltmle.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ef291f17a8c339a4"}},{"code_sha256_prefix":"aff095bd56d3f838","entry":"solve_one_dimensional_submodel","repo":"shirakawatoru/dltmle-icml-2024","repo_kind":"listed","path":"src/model/LAY/dltmle.py","file_url":"https://github.com/shirakawatoru/dltmle-icml-2024/blob/HEAD/src/model/LAY/dltmle.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"aff095bd56d3f838"}},{"code_sha256_prefix":"7b616b5cef569983","entry":"DeepLTMLE","repo":"shirakawatoru/dltmle-icml-2024","repo_kind":"listed","path":"src/model/LAY/dltmle.py","file_url":"https://github.com/shirakawatoru/dltmle-icml-2024/blob/HEAD/src/model/LAY/dltmle.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7b616b5cef569983"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}