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We consider the following question: given information on individual policyholder characteristics, how can we ensure that insurance prices do not discriminate with respect to protected characteristics, such as gender? We address the issues of direct and indirect discrimination, the latter resulting from implicit learning of protected characteristics from nonprotected ones. We provide rigorous mathematical definitions for direct and indirect discrimination, and we introduce a simple formula for discrimination-free pricing, that avoids both direct and indirect discrimination. Our formula works in any statistical model. We demonstrate its application on a health insurance example, using a state-of-the-art generalized linear model and a neural network regression model. An important conclusion is that discrimination-free pricing in general requires collection of policyholders’ discriminatory characteristics, posing potential challenges in relation to policyholder’s privacy concerns.
If case study comparison is useful in the social sciences, it should be at least as useful as a way of understanding the political preferences and participation of individuals as it is for larger and more complex social categories such as organizations, parties, militaries, or states. Thus, qualitative comparisons involving the political preferences and activity of American billionaires will serve as a benchmark for the plausibility of various comparative frameworks. The chapter will demonstrate that applying Mill’s method-type comparison to billionaires generates inferential absurdities and also shows that regression-type logics of control are a poor fit for individual-level qualitative analysis. More feasible frameworks for understanding qualitative comparison at the individual level come both earlier and later in the inferential process. Cross-case comparisons are highly valuable for concept formation and theory building prior to any systematic effort at causal inference and also play a meaningful role in inferences about moderation effects after process tracing has been successful within each case. Furthermore, multi-case qualitative analysis that is not structured as explicit comparison can make crucial contributions to multi-method research on individuals.
In light of important political events that go beyond the nation state (e.g., migration, climate change, and the coronavirus pandemic), domestic politicians are increasingly pressured to scrutinize and speak out on European policy-making. This creates a potential trade-off between allocating effort to domestic and supranational affairs, respectively. We examine how citizens perceive legislator involvement in European Union (EU) politics with a pre-registered conjoint experiment in Germany. Our results show that Members of Parliament (MPs) are not disadvantaged when allocating effort to European affairs as compared to local and national affairs. In addition, voters tend to prefer MPs who engage in EU policy reform over those who do not. As demand for legislator involvement in European politics is on the rise, we provide empirical evidence that MPs can fulfill this demand without being disadvantaged by the electorate.
In a recent article, I argued that the Bayesian process tracing literature exhibits a persistent disconnect between principle and practice. In their response, Bennett, Fairfield, and Charman raise important points and interesting questions about the method and its merits. This letter breaks from the ongoing point-by-point format of the debate by asking one question: In the most straightforward case, does the literature equip a reasonable scholar with the tools to conduct a rigorous analysis? I answer this question by walking through a qualitative Bayesian analysis of the simplest example: analyzing evidence of a murder. Along the way, I catalogue every question, complication, and pitfall I run into. Notwithstanding some important clarifications, I demonstrate that aspiring practitioners are still facing a method without guidelines or guardrails.
This paper proposes a Bayesian alternative to the synthetic control method for comparative case studies with a single or multiple treated units. We adopt a Bayesian posterior predictive approach to Rubin’s causal model, which allows researchers to make inferences about both individual and average treatment effects on treated observations based on the empirical posterior distributions of their counterfactuals. The prediction model we develop is a dynamic multilevel model with a latent factor term to correct biases induced by unit-specific time trends. It also considers heterogeneous and dynamic relationships between covariates and the outcome, thus improving precision of the causal estimates. To reduce model dependency, we adopt a Bayesian shrinkage method for model searching and factor selection. Monte Carlo exercises demonstrate that our method produces more precise causal estimates than existing approaches and achieves correct frequentist coverage rates even when sample sizes are small and rich heterogeneities are present in data. We illustrate the method with two empirical examples from political economy.
Regression discontinuity (RD) designs have become increasingly popular in political science, due to their ability to showcase causal effects under weak assumptions. This paper provides an intuition-based guide for the use of the RD in applied research. After an intuitive explanation of how the method works, we provide a checklist that can help researchers understand the main robustness checks they should run, and a quick introduction to software implementing the design. We also provide a list of classic designs and examples of their application in political science. We hope this article can constitute a stepping stone from which researchers interested in RD can jump to more advanced literature; and which makes researchers not interested in implementing RDs better consumers of research employing this design.
In the last decades, ‘research design’ has become a strategic topic across political science. An emerging discourse relies on it to encompass paradigmatic oppositions and cultivate a pluralist approach to causation. As an introduction to the special issue on the topic, we offer an outline of the roles that the discipline recognizes to design in its relation to models and contend that, in a time of fascination for predictors, political science pluralism allows for balancing interpretability and validity of findings at once.
This essay examines certain epistemic problems facing administrative states’ efforts to draft efficient regulations for their societies. I argue that a basic feature of the administrative state’s authority, namely its monopoly over the production of legally binding rules for all members of a geographically defined society, creates epistemic problems that impede efficient rule-making. Specifically, the administrative state’s monopoly over the production of legally binding rules prevents multiple public policies from being simultaneously implemented and compared. The resulting singularity of administrative states’ regulatory decisions prevents observation of the counterfactual effects of policies that were possible but which were not implemented. The absence of observable policy counterfactuals frustrates efforts to assess the efficiency of administrative states’ decisions, as it is impossible to determine whether different policies would have generated greater benefits at lower cost than the policy the state implemented. As these epistemic problems are derived from the singularity of administrative states’ decisions, they exist independently of principal agent problems, suboptimal incentives, or the preferences and capabilities of administrative personnel.
The article offers an overview of the use of survey experiments in political research by relying on available examples, bibliographic data and a content analysis of experimental manuscripts published in leading academic journals over the last two decades. After a short primer to the experimental approach, we discuss the development, applications and potential problems to internal and external validity in survey experimentation. The article also provides original examples, contrasting a traditional factorial and a more innovative conjoint design, to show how survey experiments can be used to test theory on relevant political topics. The main challenges and possibilities encountered in envisaging, planning and implementing survey experiments are examined. The article outlines the merits, limits and implications of the use of the experimental method in political research.
Experiments are increasingly used to better understand various aspects of civil conflict. A critical barrier to peace is often conflict recurrence after a settlement or other attempt to end fighting between sides. This chapter examines the growing literature on experiments in post-conflict contexts to understand their contributions and limitations to our understanding of the dynamics in this period. It argues that work on post-conflict contexts takes two different perspectives: a peace stabilization approach emphasizes special problems from civil conflict, including how to sustain peace agreements, while a peace consolidation approach emphasizes problems common to statebuilding, including how to reconstruct communities. Both seek in part to prevent conflict recurrence, though, and that is the focus of this chapter. Although more existing theory links stabilization programs with enduring peace, more existing experiments examine consolidation programs. Both approaches would benefit from new work. Post-conflict contexts in general, however, are difficult environments in which to work, and so experiments face three interrelated challenges: first, these contexts present special ethical challenges due to both the high stakes of peace and the sensitivity of subjects; second, these are complex treatments often conducted simultaneously by different actors, and these are treatments that depend on both institutional change and behavioral responses, so change is the constant in these contexts; and, third, these contexts also face heterogeneity in terms of programs but also contexts that mean the lessons may not travel even among post-conflict settings. Despite these challenges, experiments in post-conflict contexts hold promise for advancing our understanding of enduring peace.
We present current methods for estimating treatment effects and spillover effects under “interference”, a term which covers a broad class of situations in which a unit’s outcome depends not only on treatments received by that unit, but also on treatments received by other units. To the extent that units react to each other, interact, or otherwise transmit effects of treatments, valid inference requires that we account for such interference, which is a departure from the traditional assumption that units’ outcomes are affected only by their own treatment assignment. Interference and associated spillovers may be a nuisance or they may be of substantive interest to the researcher. In this chapter, we focus on interference in the context of randomized experiments. We review methods for when interference happens in a general network setting. We then consider the special case where interference is contained within a hierarchical structure. Finally, we discuss the relationship between interference and contagion. We use the interference R package and simulated data to illustrate key points. We consider efficient designs that allow for estimation of the treatment and spillover effects and discuss recent empirical studies that try to capture such effects.
Does diplomacy affect the prospects of international conflict and cooperation? Systematic empirical assessment has been hindered by the inferential challenges of separating diplomacy from the distribution of power and interests that underlies its conduct. This paper addresses the question of diplomacy's efficacy by examining the intragovernmental politics of US foreign policy, and the varying influence of diplomatic personnel in the policy process. I claim that diplomats hold the strongest preferences for cooperative relations with their host countries, relative to other participants in the foreign policy process. They also exert substantial influence over the formation and implementation of US policies toward their host countries but their influence is intermittently weakened by the short-term shock of an ambassadorial turnover. As a result, when ambassadors are removed from post, diplomacy is more likely to be eschewed for more conflictual means of settling international disagreements, and opportunities for economic exchange are less likely to be realized. I test this theory using newly collected data on US diplomatic representation, for the global sample of countries from 1960 through 2014. To address concerns of diplomatic staffing being endogenous to political interests, I leverage a natural experiment arising from the State Department's three-year ambassadorial rotation system. The turnover of a US ambassador causes a decrease in US exports to the country experiencing the turnover, and heightens the risk of onset of a militarized dispute between that country and the US. These findings point to bureaucratic delegation as an important but overlooked determinant of macro-level international outcomes.
Ten years since the publication of the first edition of this handbook two things are clear: The world is no less complicated than it was a decade ago and we are better at designing, running, and analyzing experiments today than we were then. In light of these observations, in this chapter I highlight the areas in which political scientists and their collaborators have excelled and how they have done so; but I also point out the challenges –in fact, in some cases, the pure limitations – that remain. Still, the prescription is for more work, more science, and more explanation in the service of reducing the apparent chaos of the interactions between the people and institutions around us.
The concept of identity has long captured the interest of scholars, and its importance in both the social sciences and in society more broadly continues to rise. As the literature surrounding identity has expanded, increased attention has been given to experimental designs that measure the concept, consequences, and correlates of identity. This chapter focuses on racial and ethnic identity within the context of experimental methods from both an analytical and methodological perspective. First, the chapter provides an overview of scholarship on the study of identity, highlighting the importance of social identity theory as the starting point for a long trajectory of theoretical and empirical work. Next, design challenges and opportunities are addressed, with specific attention paid to the conceptual use of identity as a variable. The following section provides examples of experimental research on racial and ethnic identity, focusing on ingroup and outgroup studies, and studies that measure political outcomes related to race and ethnicity. One common shortcoming of identity research is the tendency to use group membership as a proxy for group identity and group consciousness, or to use the terms interchangeably when they are in fact theoretically distinct concepts. I argue that experimental designs may demonstrate the need to disentangle group membership from group identity and group consciousness, and offer a strategy for adapting measurement tools to study identity. The chapter concludes by providing recommendations and identifying areas for future research to expand our understanding of racial and ethnic identity through the use of experiments.
Experiments often focus on recovering an average effect of a treatment on an outcome. A subgroup analysis involves identifying subgroups of observations for which the treatment is particularly efficacious or deleterious. Since these subgroups are not preregistered but instead discovered from the data, significant inferential issues emerge. We discuss methods for conduct honest inference on subgroups, meaning generating valid p-values and confidence intervals which account for the fact that the subgroups were not specified a priori. Central to this approach is the split-sample strategy, where half the data is used to identify effects and the other half to test them. After an intuitive and formal discussion of these issues, we provide simulation evidence and two examples illustrating these concepts in practice.
Experimental political science has changed. In two short decades, it evolved from an emergent method to an accepted method to a primary method. The challenge now is to ensure that experimentalists design sound studies and implement them in ways that illuminate cause and effect. Ethical boundaries must also be respected, results interpreted in a transparent manner, and data and research materials must be shared to ensure others can build on what has been learned. This book explores the application of new designs; the introduction of novel data sources, measurement approaches, and statistical methods; the use of experiments in more substantive domains; and discipline-wide discussions about the robustness, generalizability, and ethics of experiments in political science. By exploring these novel opportunities while also highlighting the concomitant challenges, this volume enables scholars and practitioners to conduct high-quality experiments that will make key contributions to knowledge.
Problems in learning that sights, sounds, or situations that were once associated with danger have become safe (extinction learning) may explain why some individuals suffer prolonged psychological distress following traumatic experiences. Although simple learning models have been unable to provide a convincing account of why this learning fails, it has recently been proposed that this may be explained by individual differences in beliefs about the causal structure of the environment.
Here, we tested two competing hypotheses as to how differences in causal inference might be related to trauma-related psychopathology, using extinction learning data collected from clinically well-characterised individuals with varying degrees of post-traumatic stress (N = 56). Model parameters describing individual differences in causal inference were related to multiple post-traumatic stress disorder (PTSD) and depression symptom dimensions via network analysis.
Individuals with more severe PTSD were more likely to assign observations from conditioning and extinction stages to a single underlying cause. Specifically, greater re-experiencing symptom severity was associated with a lower likelihood of inferring that multiple causes were active in the environment.
We interpret these results as providing evidence of a primary deficit in discriminative learning in participants with more severe PTSD. Specifically, a tendency to attribute a greater diversity of stimulus configurations to the same underlying cause resulted in greater uncertainty about stimulus-outcome associations, impeding learning both that certain stimuli were safe, and that certain stimuli were no longer dangerous. In the future, better understanding of the role of causal inference in trauma-related psychopathology may help refine cognitive therapies for these disorders.
The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an underestimate or overestimate of the effect of interest. Various advanced statistical approaches exist that offer certain advantages in terms of addressing these potential biases. However, although these statistical approaches have different underlying statistical assumptions, in practice they cannot always completely remove key sources of bias; therefore, using design-based approaches to improve causal inference is also important. Here it is the design of the study that addresses the problem of potential bias – either by ensuring it is not present (under certain assumptions) or by comparing results across methods with different sources and direction of potential bias. The distinction between statistical and design-based approaches is not an absolute one, but it provides a framework for triangulation – the thoughtful application of multiple approaches (e.g. statistical and design based), each with their own strengths and weaknesses, and in particular sources and directions of bias. It is unlikely that any single method can provide a definite answer to a causal question, but the triangulation of evidence provided by different approaches can provide a stronger basis for causal inference. Triangulation can be considered part of wider efforts to improve the transparency and robustness of scientific research, and the wider scientific infrastructure and system of incentives.
The democratic peace—the idea that democracies rarely fight one another—has been called “the closest thing we have to an empirical law in the study of international relations.” Yet, some contend that this relationship is spurious and suggest alternative explanations. Unfortunately, in the absence of randomized experiments, we can never rule out the possible existence of such confounding biases. Rather than commonly used regression-based approaches, we apply a nonparametric sensitivity analysis. We show that overturning the negative association between democracy and conflict would require a confounder that is forty-seven times more prevalent in democratic dyads than in other dyads. To put this number in context, the relationship between democracy and peace is at least five times as robust as that between smoking and lung cancer. To explain away the democratic peace, therefore, scholars would have to find far more powerful confounders than those already identified in the literature.
We use nationwide deed-level records on home foreclosures to examine the effects of economic distress on electoral outcomes and individual voter turnout. County-level difference-in-differences estimates show that counties that suffered larger increases in foreclosures did not punish or reward members of the incumbent president's party more than less affected counties. Linking the Ohio voter file to individual foreclosures, difference-in-differences estimates show that individuals whose homes were foreclosed on were less likely to turn out, rather than being mobilized. However, in 2016 counties more exposed to foreclosures supported Trump at substantially higher rates. Taken together, the evidence suggests that the effect of local economic distress on incumbent performance is generally close to zero and only becomes substantial in unusual circumstances.