While much of the causal inference literature has focused on addressing internal validity biases, both internal and external validity are necessary for unbiased estimates in a target population of interest. However, few generalizability approaches exist for estimating causal quantities in a target population that is not well-represented by a randomized study but is reflected when additionally incorporating observational data. To generalize to a target population represented by a union of these data, we propose a novel class of conditional cross-design synthesis estimators that combine randomized and observational data, while addressing their estimates' respective biases-lack of overlap and unmeasured confounding.
View Article and Find Full Text PDFHealth Aff (Millwood)
February 2017
The Affordable Care Act (ACA) dramatically expanded the use of regulated marketplaces in health insurance, but consumers often fail to shop for plans during open enrollment periods. Typically these consumers are automatically reenrolled in their old plans, which potentially exposes them to unexpected increases in their insurance premiums and cost sharing. We conducted a randomized intervention to encourage enrollees in an ACA Marketplace to shop for plans.
View Article and Find Full Text PDFUnder the Affordable Care Act, the risk-adjustment program is designed to compensate health plans for enrolling people with poorer health status so that plans compete on cost and quality rather than the avoidance of high-cost individuals. This study examined health plan incentives to limit covered services for mental health and substance use disorders under the risk-adjustment system used in the health insurance Marketplaces. Through a simulation of the program on a population constructed to reflect Marketplace enrollees, we analyzed the cost consequences for plans enrolling people with mental health and substance use disorders.
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