Conditional Cross-Design Synthesis Estimators for Generalizability in Medicaid
This addresses the challenge of external validity in causal inference for Medicaid policy, representing an incremental advancement in generalizability methods.
The paper tackles the problem of generalizing causal estimates to a target population by combining randomized and observational data, proposing conditional cross-design synthesis estimators that address biases, and applies them to estimate the effect of managed care plans on healthcare spending among Medicaid beneficiaries in New York City, though no concrete numbers are provided in the abstract.
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 when the target population 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. 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 apply these methods to estimate the causal effect of managed care plans on health care spending among Medicaid beneficiaries in New York City.