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Below results based on the criteria 'potential outcomes'
Total number of records returned: 3
Why Process Matters for Causal Inference
It is often assumed that the only way to assess the causal effects of an explanatory variable on an outcome variable is to compare the outcomes from units with differing values of the explanatory variable. In this paper, we provide a formal account of how within-unit causal process information (i.e., knowledge of the causal chain linking an explanatory variable to an outcome variable) can be used to make certain types of causal inferences without comparing outcomes from units with differing values of the explanatory variable. The methods discussed in this paper allow causal researchers to make full use of causal information that many had heretofore ignored. At the same time, because these methods are embedded in a Bayesian potential outcomes causal model, researchers are held to high standards of transparency and logical consistency. We illustrate these methods with an application to the effects of election day registration on African American turnout. This analysis shows that previous regression or matching estimates for these effects are likely overstated.
Unpacking the Black Box: Learning about Causal Mechanisms from Experimental and Observational Studies
direct and indirect effects
Understanding causal mechanisms is a fundamental goal of social science research. Demonstrating whether one variable causes a change in another is often insufficient, and researchers seek to explain why such a causal relationship arises. Nevertheless, little is understood about how to identify causal mechanisms in empirical research. Many researchers either informally talk about possible causal mechanisms or attempt to quantify them without explicitly stating the required assumptions. Often, some assert that process tracing in detailed case studies is the only way to evaluate causal mechanisms. Others contend the search for causal mechanisms is so elusive that we should instead focus on causal effects alone. In this paper, we show how to learn about causal mechanisms from experimental and observational studies. Using the potential outcomes framework of causal inference, we formally define causal mechanisms, present general identification and estimation strategies, and provide a method to assess the sensitivity of one's conclusions to the possible violations of key identification assumptions. We also propose several alternative research designs for both experimental and observational studies that may help identify causal mechanisms under less stringent assumptions. The proposed methodology is illustrated using media framing experiments and observational studies of incumbency advantage.
Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments
average marginal component effects
fractional factorial design
For decades, market researchers have used conjoint analysis to understand how consumers make decisions when faced with multi-dimensional choices. In such analyses, respondents are asked to score or rank a set of alternatives, where each alternative is defined by multiple attributes which are varied randomly or intentionally. Political scientists are frequently interested in parallel questions about decision-making, yet to date conjoint analysis has seen little use within the field. In this manuscript, we demonstrate the potential value of conjoint analysis in political science, using examples about vote choice and immigrant admission to the United States. In doing so, we develop a set of statistical tools for drawing causal conclusions from stated preference data based on the potential outcomes framework of causal inference. We discuss the causal estimands of interest and provide a formal analysis of the assumptions required for identifying those quantities. Prior conjoint analyses have typically used designs which limit the number of unique conjoint profiles. We employ a survey experiment to compare this approach to a fully randomized approach. Both our formal analysis of the causal estimands and our empirical results highlight the potential biases of common approaches to conjoint analysis which restrict the number of profiles.