Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto. (2011). ``Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies.'' American Political Science Review, Vol. 105, No. 4 (November), pp. 765-789.



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.

  Other information

The project page for the statistical analysis of causal mechanism: Webpage.
We have developed easy-to-use software and have written a paper that explains its use with some examples: Imai, Kosuke, Luke Keele, Dustin Tingley and Teppei Yamamoto. ``Causal Mediation Analysis Using R.''
This paper was discussed at the Development Impact Blog at the World Bank.

© Kosuke Imai
 Last modified: Mon Oct 24 10:22:36 EDT 2011