  Statisticians have made great progress in creating methods that reduce our
reliance on parametric assumptions. However this explosion in research has
resulted in a breadth of inferential strategies that both create opportunities
for more reliable inference as well as complicate the choices that an applied
researcher has to make and defend. Relatedly, researchers advocating for new
methods typically compare their method to at best 2 or 3 other causal inference
strategies and test using simulations that may or may not be designed to
equally tease out flaws in all the competing methods. The causal inference data
analysis challenge, "Is Your SATT Where It's At?", launched as part of the 2016
Atlantic Causal Inference Conference, sought to make progress with respect to
both of these issues. The researchers creating the data testing grounds were
distinct from the researchers submitting methods whose efficacy would be
evaluated. Results from 30 competitors across the two versions of the
competition (black box algorithms and do-it-yourself analyses) are presented
along with post-hoc analyses that reveal information about the characteristics
of causal inference strategies and settings that affect performance. The most
consistent conclusion was that methods that flexibly model the response surface
perform better overall than methods that fail to do so. Finally new methods are
proposed that combine features of several of the top-performing submitted
methods.
