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Replication data for: Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication data for: Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies
 
Identifier https://doi.org/10.7910/DVN/X73I3J
 
Creator Kosuke Imai
Luke Keele
Dustin Tingley
Teppei Yamamoto
 
Publisher Harvard Dataverse
 
Description 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. Randomizing treatment and intermediate variables is also insufficient. Despite these difficulties, study of causal mechanisms is too important to abandon. We make three contributions to improve research on causal mechanisms. First, we present a minimum set of assumptions required under standard designs of experimental and observational studies and develop a general algorithm for estimating causal mediation effects. Second, we provide a method to assess sensitivity of conclusions to potential violations of a key assumption. Third, we offer alternative research designs for identifying causal mechanisms under weaker assumptions. The proposed approach is illustrated using media framing experiments and incumbency advantage studies
 
Date 2011