A Framework for Dynamic Causal Inference in Political Science

Dynamic strategies are an essential part of politics. In the context of campaigns, for example, candidates continuously recalibrate their campaign strategy in response to polls and opponent actions. Traditional causal inference methods, however, assume that these dynamic decisions are made all at once, an assumption that forces a choice between omitted variable bias and posttreatment bias. Thus, these kinds of single-shot causal inference methods are inappropriate for dynamic processes like campaigns. I resolve this dilemma by adapting methods from biostatistics, thereby presenting a holistic framework for dynamic causal inference. I then use this method to estimate the effectiveness of an inherently dynamic process: a candidate's decision to go negative. Drawing on U.S. statewide elections (20002006), I find, in contrast to the previous literature and alternative methods, that negative advertising is an effective strategy for nonincumbents. I also describe a set of diagnostic tools and an approach to sensitivity analysis.