doi:10.1017/S0003055411000414 Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies

Identifying causal mechanisms is a fundamental goal of social science. Researchers seek to study notonly whether one variable affects another but also how such a causal relationship arises. Yet com-monly 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, the 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 for assessing the 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. Over the last couple of decades, social scientistshave given greater attention tomethodologicalissues related to causation. This trend has led to a growing number of laboratory, field, and survey experiments, as well as an increasing use of natural experiments, instrumental variables, and quasirandom-ized studies such as regression discontinuity designs. However, many of these empirical studies focus o