Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach
A main research goal in various studies is to use an observational data set and provide a new set of counterfactual guidelines that can yield causal improvements.
A main research goal in various studies is to use an observational data set and provide a new set of counterfactual guidelines that can yield causal improvements.
There is a growing amount of evidence that machine learning (ML) algorithms can be used to develop accurate clinical risk scores for a wide range of medical conditions.
The number of disability beneficiaries has doubled in the past two decades. It is difficult to determine how much is explained by changes in health, as we lack a counterfactual.
Regulators of new products confront a tradeoff between speeding a new product to market and collecting additional product quality information.
Health plans for the poor increasingly limit access to specialty hospitals. We investigate the role of adverse selection in generating this equilibrium among private plans in Medicaid.
We examine differences in mobility outcomes between residents of highest and lowest socio-economic index (SEI) at the Census block group (CBG) level in nine major US cities prior to and during the COV
Context: To what extent does pharmaceutical revenue growth depend on new medicines versus increasing prices for existing medicines?
In various organizations including hospitals, individuals are not forced to follow specific assignments, and thus, deviations from preferred task assignments are common.
The U.S. healthcare system is undergoing a period of substantial change, with hospitals purchasing many physician practices (\vertical integration").
COVID-19 vaccines are widely available in wealthy countries, yet many people remain unvaccinated.
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