AVERAGED PREDICTION MODELS (APM): IDENTIFYING CAUSAL EFFECTS IN CONTROLLED PRE-POST SETTINGS WITH APPLICATION TO GUN POLICY.

To investigate causal impacts, many researchers use controlled pre-post designs that compare over-time differences between a population exposed to a policy change and an unexposed comparison group. However, researchers using these designs often disagree about the "correct" specification of the causal model, perhaps most notably in analyses to identify the effects of gun policies on crime. To help settle these model specification debates, we propose a general identification framework that unifies a variety of models researchers use in practice. In this framework, which nests "brand name" designs like Difference-in-Differences as special cases, we use models to predict untreated outcomes and then correct the treated group's predictions using the comparison group's observed prediction errors. Our point identifying assumption is that treated and comparison groups would have equal prediction errors (in expectation) under no treatment. To choose among candidate models, we propose a data-driven procedure based on models' robustness to violations of this point identifying assumption. Our selection procedure averages over candidate models, weighting by each model's posterior probability of being the most robust given its differential average prediction errors in the pre-period. This approach offers a way out of debates over the "correct" model by choosing on robustness instead and has the desirable property of being feasible in the "locked box" of pre-intervention data only. We apply our methodology to the gun policy debate, focusing specifically on Missouri's 2007 repeal of its permit-to-purchase law, and provide an R package (apm) for implementation.