{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T06:06:14Z","timestamp":1784786774929,"version":"3.55.0"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T00:00:00Z","timestamp":1784764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Public health policies are implemented within legal and regulatory environments associated with heterogeneous economic and health related outcomes across regions and time. Existing policy evaluation approaches often have limited capacity to represent regional heterogeneity, temporal policy patterns, and uncertainty in observed policy associated outcome changes. To address this issue, this study proposes a predictive computational framework for estimating subsequent economic outcome changes under observed public health policy, legal, and regulatory conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The empirical task is formulated as supervised temporal regression using region time observations constructed from the Oxford COVID-19 Government Response Tracker and COVID-19 US State Policy datasets. The proposed Counterfactual Policy Optimizer (CPO) represents legal frameworks as policy constraints and regulatory variables, and combines manifold constraint regularization, agent driven policy interaction modeling, and probabilistic outcome forecasting. In this study, the term counterfactual refers to model based scenario comparison within the observed data distribution and feasible policy space, and does not imply causal identification in the econometric or structural causal sense.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results and discussion<\/jats:title>\n                    <jats:p>The training objective integrates prediction accuracy, uncertainty modeling, utility based policy comparison, and constraint regularization. Experimental results indicate that the proposed framework achieves lower prediction error and stronger trend consistency than traditional econometric, machine learning, and temporal deep learning baselines under the same observational prediction setting. Additional policy related indicators indicate that legal feasibility, implementation conditions, and uncertainty aware forecasting can support structured predictive evaluation of public health policy associated economic outcome changes. The study provides a reproducible framework for examining temporal associations between legally structured public health policy conditions and subsequent economic outcome changes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1781611","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T05:38:59Z","timestamp":1784785139000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive estimation of economic outcome changes associated with legal frameworks and public health policy conditions"],"prefix":"10.3389","volume":"14","author":[{"given":"Peiran","family":"Lu","sequence":"first","affiliation":[{"name":"Law School, Beijing Foreign Studies University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songling","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of 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