Although published works rarely include causal estimates from more
than a few model specifications, authors usually choose the
presented estimates from numerous trial runs readers never see.
Given the often large variation in estimates across choices of
control variables, functional forms, and other modeling assumptions,
how can researchers ensure that the few estimates presented are
accurate or representative? How do readers know that publications
are not merely demonstrations that it is possible to find a
specification that fits the author's favorite hypothesis? And how
do we evaluate or even define statistical properties like
unbiasedness or mean squared error when no unique model or estimator
even exists? Matching methods, which offer the promise of causal
inference with fewer assumptions, constitute one possible way
forward, but crucial results in this fast-growing methodological
literature are often grossly misinterpreted. We explain how to
avoid these misinterpretations and propose a unified approach that
makes it possible for researchers to preprocess data with matching
(such as with the easy-to-use software we offer) and then to apply
the best parametric techniques they would have used anyway. This
procedure makes parametric models produce more accurate and
considerably less model-dependent causal inferences.
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