Posttreatment Bias Research Articles

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 (2000–2006), 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.

Causal Effects of Perceived Immutable Characteristics

Abstract Despite their ubiquity, observational studies to infer the causal effect of a so-called immutable characteristic, such as race or sex, have struggled for coherence, given the unavailability of a manipulation analogous to a “treatment” in a randomized experiment and the danger of posttreatment bias. We demonstrate that a shift in focus from actual traits to perceptions of them can address both of these issues while facilitating articulation of other critical concepts, particularly the timing of treatment assignment. We illustrate concepts by discussing the designs of various studies of the role of race in trial court death penalty decisions.

Avoiding Randomization Failure in Program Evaluation, with Application to the Medicare Health Support Program

We highlight common problems in the application of random treatment assignment in large-scale program evaluation. Random assignment is the defining feature of modern experimental design, yet errors in design, implementation, and analysis often result in real-world applications not benefiting from its advantages. The errors discussed here cover the control of variability, levels of randomization, size of treatment arms, and power to detect causal effects, as well as the many problems that commonly lead to post-treatment bias. We illustrate these issues by identifying numerous serious errors in the Medicare Health Support evaluation and offering recommendations to improve the design and analysis of this and other large-scale randomized experiments.