Using Multiple Comparison Groups to Address Unobserved Biases in Comparative Effectiveness Research

Studies of large policy interventions typically do not involve randomization. Adjustments, such as matching, can remove the bias due to observed covariates, but residual confounding remains a concern. In this paper we introduce two analytical strategies to bolster inferences of the effectiveness of policy interventions based on observational data.

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Year of publication:	2011-09-01
Authors:	Yoon, Frank B. ; Huskamp, Haiden A. ; Busch, Alisa B. ; Normand, Sharon-Lise T.
Institutions:	Mathematica Policy Research
Subject:	Causal inference \| Fine balance \| Quasi-experiments \| Testing in order

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Extent:	text/html
Series:	Mathematica Policy Research Reports.
Type of publication:	Book / Working Paper
Classification:	I - Health, Education, and Welfare
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011101127