Beyond Toplines: Heterogeneous Treatment Effects in Randomized Experiments∗

Randomized experiments have become increasingly important for political scientists and campaign professionals. With few exceptions, these experiments have addressed the overall causal effect of an intervention across the entire population, known as the average treatment effect (ATE). A much broader set of questions can often be addressed by allowing for heterogeneous treatment effects. We discuss methods for estimating such effects developed in other disciplines and introduce key concepts, especially the conditional average treatment effect (CATE), to the analysis of randomized experiments in political science. We expand on this literature by proposing an application of generalized additive models (GAMs) to estimate non-linear heterogeneous treatment effects. We demonstrate the practical importance of these techniques by re-analyzing a major experimental study on voter mobilization and social pressure and a recent randomized experiment on voter registration and text messaging from the 2008 US election.