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

ABSTRACT: 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.

SUBMITTER: Leavitt T 

PROVIDER: S-EPMC12633725 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature