Dustin Tingley

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Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability(More)
Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study not only whether one variable affects another but also how such a causal relationship arises. Yet commonly used statistical methods for identifying causal mechanisms rely upon untestable assumptions and are often inappropriate even under those assumptions.(More)
In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently(More)
Monoamine oxidase A gene (MAOA) has earned the nickname "warrior gene" because it has been linked to aggression in observational and survey-based studies. However, no controlled experimental studies have tested whether the warrior gene actually drives behavioral manifestations of these tendencies. We report an experiment, synthesizing work in psychology and(More)
Mediation analysis aims to uncover causal pathways along which changes are transmitted from stimulus to response. Recent advances in causal inference have given rise to a general and easy-to-use estimator for assessing the extent to which the effect of one variable on another is mediated by a third, thus setting a causally-sound standard for mediation(More)
Estimating the mechanisms that connect explanatory variables with the explained variable, also known as “mediation analysis,” is central to a variety of social science fields, especially psychology, and increasingly fields like epidemiology. Recent work on the statistical methodology behind mediation analysis points to limitations in earlier methods. We(More)
Experimentation is a powerful methodology that enables scientists to empirically establish causal claims. However, one important criticism is that experiments merely provide a black-box view of causality and fail to identify causal mechanisms. Specifically, critics argue that although experiments can identify average causal effects, they cannot explain the(More)
We develop the Structural Topic Model which provides a general way to incorporate corpus structure or document metadata into the standard topic model. Document-level covariates enter the model through a simple generalized linear model framework in the prior distributions controlling either topical prevalence or topical content. We demonstrate the model’s(More)
Despite broad use of surveys and survey experiments within political science, the vast majority of survey analysis deals with responses to options along a scale or from preestablished categories. Yet, in most areas of life individuals communicate either by writing or by speaking, a fact reflected in earlier debates about open and closed-ended survey(More)