How Much is Minnesota Like Wisconsin? Assumptions and Counterfactuals in Causal Inference with Observational Data∗

Political scientists are often interested in estimating causal effects. Identification of causal estimates with observational data invariably requires strong untestable assumptions. Here, we outline a number of the assumptions used in the extant empirical literature. We argue that these assumptions require careful evaluation within the context of specific applications. To that end, we present an empirical case study on the effect of Election Day registration on turnout. We show how different identification assumptions lead to different answers, and that many of the standard assumptions used are implausible. Specifically, we show that EDR likely had little effect on turnout in any state including Minnesota and Wisconsin. We conclude with an argument for stronger research designs.