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Large-scale models require substantial computational resources for analysisand studying treatment conditions. Specifically, estimating treatment effectsusing simulations may require a lot of infeasible resources to allocate atevery treatment condition. Therefore, it is essential to develop efficientmethods to allocate computational resources for estimating treatment effects.Agent-based simulation allows us to generate highly realistic simulationsamples. FRED (A Framework for Reconstructing Epidemiological Dynamics) is anagent-based modeling system with a geospatial perspective using a syntheticpopulation constructed based on the U.S. census data. Given its syntheticpopulation, FRED simulations present a baseline for comparable results fromdifferent treatment conditions and treatment conditions. In this paper, we showthree other methods for estimating treatment effects. In the first method, weresort to brute-force allocation, where all treatment conditions have an equalnumber of samples with a relatively large number of simulation runs. In thesecond method, we try to reduce the number of simulation runs by customizingindividual samples required for each treatment effect based on the width ofconfidence intervals around the mean estimates. In the third method, we use aregression model, which allows us to learn across the treatment conditions suchthat simulation samples allocated for a treatment condition will help betterestimate treatment effects in other conditions. We show that theregression-based methods result in a comparable estimate of treatment effectswith less computational resources. The reduced variability and fasterconvergence of model-based estimates come at the cost of increased bias, andthe bias-variance trade-off can be controlled by adjusting the number of modelparameters (e.g., including higher-order interaction terms in the regressionmodel).

Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder