Observing the Counterfactual? The Search for Political Experiments in Nature (original) (raw)
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Political Science 590 Political Experiments: Design & Analysis, Part I
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Overview Randomized interventions allow political scientists to claim that comparisons are causal: randomization allows us the ability to characterize counterfactual comparisons-to say how the treated group would have responded had treatment been withheld. Randomization also allows us to test hypotheses about causal comparisons without requiring large samples or probability models of outcomes.
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General Information Overview Randomized interventions allow political scientists to claim that comparisons are causal: randomization allows us the ability to characterize counterfactual comparisons- to say how the treated group would have responded had treatment been withheld. Randomization also allows us to test hypotheses about causal comparisons without requiring large samples or probability models of outcomes. Randomized experiments thus promise to simplify our lives and to enable clear answers to important substantive and theoretical questions without requiring much in the way of extraneous justification. Yet, there is an art in designing and analyzing a randomized experiment. Only the simplest of experiments can be analyzed simply. And, in political science, we may not be able to directly require subjects to be exposed to a dose of our treatment and may need to work indirectly, using the randomization as an instrument to manipulate a dose, perhaps at a distance, perhaps weakly...
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Natural experiments are an increasingly popular research design in political science. This popularity raises a number of questions. First, what are natural experiments and why are they appealing? Second, what makes a good natural experiment? And finally, are natural experiments able to provide resources for knowledge production that other methodologies cannot or do not provide? Using Mary Morgan’s and Thad Dunning’s recent work on natural experiments, I offer answers to the first two questions and use the analysis to argue that natural experiments highlight features of knowledge production that support methodological pluralism and the multiple aims of research.
En este "review article" se describen las ventajas de los experimentos en la ciencia política actual y se resume el estado de la cuestión en la literatura especializada, la cual apunta que el aumento que presenciamos se debe, fundamentalmente, al hecho de que los experimentos aventajan a los otros métodos en dilucidar las relaciones causa-efecto. Los experimentos han demostrado ser lo suficientemente flexibles para adaptarse a las preferencias de los investigadores, las preguntas complejas y la innovación en los diseños. Por ello, hemos presenciado la expansión de los métodos, los avances en la depuración de teorías, y el progreso en el establecimiento de relaciones causales que los experimentos han traído a la ciencia política. En el último apartado se mencionan las principales limitaciones de los diseños de investigación experimentales. Palabras claves: experimentos, diseños de investigación experimentales, validez, causalidad, generalización.
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Political scientists are increasingly using experiments to study the relationship between institutions and political and economic outcomes. Institutions are the “rules and procedures that structure social interaction by constraining and enabling actors’ behavior” (Helmke and Levitsky, 2006, 5). During more than two decades of renewed interest in institutions in political science, researchers have sought answers to broad questions, like: How do institutions affect outcomes such as growth and development, participation, accountability, and policy selection? Which institutions, and what elements of institutional design, matter for these outcomes? How do formal institutions interact with informal institutions? How can weak political institutions be strengthened? And, what are the causes of institutional change? The interest in using experiments to address such questions reflects an enduring concern with causal inference in the institutions literature (Frye, 2012). Early scholarly work e...