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February 27, 2026Biometrics

Bias mitigation in matched observational studies with continuous treatments: calipered non-bipartite matching and bias-corrected estimation and inference

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Authors

AFAngel FrazierSHSiyu HengWZWen Zhou

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Overview

Demonstrates bias correction in causal inference for continuous treatments using advanced matching techniques, suggesting enhanced estimation accuracy.

Key Points

  • The aim is to develop a framework for reducing bias in matched observational studies with continuous treatments.
  • Re-analyzed a matched observational study on social distancing and COVID-19 cases.
  • Proposed a caliper method integrating covariate and treatment dose information for improved matching.
  • Introduced a bias-corrected Neyman estimator for accurate treatment effect estimation.
  • Re-analysis showed potential severe bias from inexact matching practices.
  • Proposed methods improved covariate balance and treatment effect estimates in numerical studies.
  • An open-source R package was developed for practical application of the proposed framework.

Cite This Study

Frazier et al. (2026) studied this question.

synapsesocial.com/papers/69a135ebed1d949a99abfe0dhttps://doi.org/10.1093/biomtc/ujag022
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