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While inexact covariate matching (i.e., covariate imbalance after matching) is common in practice, previous matched studies with continuous treatments have often overlooked this issue as long as post-matching covariate balance meets certain criteria. Through re-analyzing a matched observational study on the effect of social distancing on COVID-19 case counts, we show that this routine practice can introduce severe bias for causal inference. Motivated by this finding, we propose a general framework for mitigating bias due to inexact matching in matched observational studies with continuous treatments, covering the matching, estimation, and inference stages. In the matching stage, we propose a carefully designed caliper that incorporates both covariate and treatment dose information to improve matching for downstream treatment effect estimation and inference. For the estimation and inference, we introduce a bias-corrected Neyman estimator paired with a corresponding bias-corrected variance estimator. The effectiveness of our proposed framework is demonstrated through numerical studies and a re-analysis of the aforementioned observational study on the effect of social distancing on COVID-19 case counts. An open-source $\\tt {R}$ package for implementing our framework has also been developed.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag022","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T12:51:23Z","timestamp":1769086283000},"source":"Crossref","is-referenced-by-count":0,"title":["Bias mitigation in matched observational studies with continuous treatments: calipered non-bipartite matching and bias-corrected estimation and inference"],"prefix":"10.1093","volume":"82","author":[{"given":"Anthony","family":"Frazier","sequence":"first","affiliation":[{"name":"Department of Statistics, Colorado State University , Fort Collins, CO 80523 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9313-3667","authenticated-orcid":false,"given":"Siyu","family":"Heng","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, New York University , New York, NY 10003 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