  Policy evaluation in empirical microeconomics has been focusing on estimating
the average treatment effect and more recently the heterogeneous treatment
effects, often relying on the unconfoundedness assumption. We propose a method
based on the interactive fixed effects model to estimate treatment effects at
the individual level, which allows both the treatment assignment and the
potential outcomes to be correlated with the unobserved individual
characteristics. This method is suitable for panel datasets where multiple
related outcomes are observed for a large number of individuals over a small
number of time periods. Monte Carlo simulations show that our method
outperforms related methods. To illustrate our method, we provide an example of
estimating the effect of health insurance coverage on individual usage of
hospital emergency departments using the Oregon Health Insurance Experiment
data.
