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Primary Submission Category: Generalizability/Transportability

Combining Randomized and Observational Data for Generalizability in Medicaid

Authors: Irina Degtiar, Sherri Rose,

Presenting Author: Irina Degtiar*

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 class of novel conditional cross-design synthesis estimators that combine randomized and observational data, while addressing their respective biases—lack of overlap and unmeasured confounding. The estimators include outcome regression, propensity weighting, and double robust approaches. All use the covariate overlap between the randomized and observational data to remove potential unmeasured confounding bias. We developed these methods to estimate the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City, finding substantial heterogeneity in effects on spending across plans. This has major implications for our understanding of Medicaid programs, where this heterogeneity has previously been hidden. Additionally, we demonstrate that unmeasured confounding rather than lack of overlap poses a larger concern in this setting.