Yusuke Narita, Yale University
Abstract: Algorithms produce a growing portion of decisions and
recommendations both in policy and business. Such algorithmic
decisions are natural experiments (conditionally quasi-randomly
assigned instruments) since the algorithms make decisions based only
on observable input variables. We use this observation to develop a
treatment-effect estimator for a class of stochastic and deterministic
decision-making algorithms. Our estimator is shown to be consistent
and asymptotically normal for well-defined causal effects. A key
special case of our estimator is a multidimensional regression
discontinuity design. We apply our estimator to evaluate the effect of
the Coronavirus Aid, Relief, and Economic Security (CARES) Act, where
more than $175 billion worth of relief funding is allocated to
hospitals via an algorithmic rule. Our estimates suggest that the
relief funding has little effect on COVID-19-related hospital activity
levels. Naive OLS and IV estimates exhibit substantial selection bias.