This dissertation provides three novel methodologies to the field of political science. In the first chapter, I describe how to make causal inferences in the face of dynamic strategies. Traditional causal inference methods assume that these dynamic decisions are made all at once, an assumption that forces a choice between omitted variable bias and post-treatment bias. I resolve this dilemma by adapting methods from biostatistics and use these methods to estimate the effectiveness of an inherently dynamic process: a candidate's decision to "go negative." Drawing on U.S. statewide elections (2000-2006), I find, in contrast to the previous literature, that negative advertising is an effective strategy for non-incumbents. In the second chapter, I develop a method for handling measurement error. Social scientists devote considerable effort to mitigating measurement error during data collection but then ignore the issue during analysis. Although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. This chapter develops an easy-to-use alternative without these problems as a special case of extreme measurement error and corrects for both. In the final chapter, I introduce a model for detecting changepoints in the distribution of contributions data because it allows for overdispersion, a key feature of contributions data. While many extant changepoint models force researchers to choose the number of changepoint ex ante, the game-changers model incorporates a Dirichlet process prior in order to estimate the number of changepoints along with their location. I demonstrate the usefulness of the model in data from the 2012 Republican primary and the 2008 U.S. Senate elections. / Government