  Large-scale models require substantial computational resources for analysis
and studying treatment conditions. Specifically, estimating treatment effects
using simulations may require a lot of infeasible resources to allocate at
every treatment condition. Therefore, it is essential to develop efficient
methods to allocate computational resources for estimating treatment effects.
Agent-based simulation allows us to generate highly realistic simulation
samples. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an
agent-based modeling system with a geospatial perspective using a synthetic
population constructed based on the U.S. census data. Given its synthetic
population, FRED simulations present a baseline for comparable results from
different treatment conditions and treatment conditions. In this paper, we show
three other methods for estimating treatment effects. In the first method, we
resort to brute-force allocation, where all treatment conditions have an equal
number of samples with a relatively large number of simulation runs. In the
second method, we try to reduce the number of simulation runs by customizing
individual samples required for each treatment effect based on the width of
confidence intervals around the mean estimates. In the third method, we use a
regression model, which allows us to learn across the treatment conditions such
that simulation samples allocated for a treatment condition will help better
estimate treatment effects in other conditions. We show that the
regression-based methods result in a comparable estimate of treatment effects
with less computational resources. The reduced variability and faster
convergence of model-based estimates come at the cost of increased bias, and
the bias-variance trade-off can be controlled by adjusting the number of model
parameters (e.g., including higher-order interaction terms in the regression
model).
