Causal Mediation Analysis   

Professor Li is great at explaining advanced techniques of causal mediation analysis even to students with little background in the subject matter. — ECPR Summer School participant

This course introduces participants to the concepts and techniques that help empirical researchers to unpack the black box of causality and to formally evaluate the process in which a causal variable of interest influences an outcome. The course begins with an introduction to the concepts related to causal inference and causal mediation analysis, such as causal mechanisms, potential outcomes, and average causal mediation effects (ACME). From there, it moves to research designs for identifying causal mediation effects in both experimental and observational studies, such as parallel designs, crossover designs, and designs that exploit instrumental variables and interaction terms. In addition to learning about the theory of causal mediation, participants also practice how to use the free statistical software R to carry out their own mediation analyses.


Dates

This one-week, 17.5-hour course runs Monday-Friday, 1:00-4:30 pm, July 8-12, 2019.


Instructor

Andrew X. Li (picture), Central European University


Detailed Description

Causal mechanisms are a fundamental component of social science theories. Researchers are not only interested in whether one variable causally affects another, but how such a causal relationship arises. Unfortunately, most existing research designs and empirical strategies merely aim at establishing causal effects between variables, resulting in a 'black box' approach to causality. As the black box approach has been criticized by many scholars for being atheoretical and even unscientific, the primary goal of this course is to introduce participants to a set of concepts, methods, and algorithms that helps researchers empirically investigate the causal mechanisms underlying social science theories.

The course is divided into three parts. The first part involves a through discussion of the concepts and frameworks that are essential for understanding and conducting causal mediation analysis. Participants are introduced to such concepts as potential outcome, average treatment effect (ATE), and average causal mediation effect (ACME) as well as the assumptions required for the identification of these causal quantities. The second part focuses on the research designs and methods that allow researchers to identify and estimate causal mediation effects. Participants learn both the experimental approach to causal mediation and mediation analysis using observational data. The final part or the course focuses on a wide variety of applications of these concepts and empirical strategies in the context of substantive research. Participants have the opportunity to explore first-hand how the different methods covered in this course can be used to provide answers to research questions about causal mechanisms.

During hands-on lab sessions, participants learn and practice how to carry out causal mediation analyses using the mediation package in R. This free software package relies on a general algorithm that can be applied to any type of data, and participants are encouraged to bring their own data – binary, ordinal, or continuous – for analysis.


Prerequisites

This course makes use of insights and techniques from several areas of social science research methodology. As a consequence, participants should have a basic background in causal inference, regression analysis (cf. Regression Analysis), and/or experimental methods (cf. Experimental Methods). Prior experience with the statistical software R would also be helpful. However, participants unfamiliar with these concepts, tools, and methods will be able to effectively participate in the course.


Requirements

Participants are expected to bring a WiFi-enabled laptop computer. Access to data, temporary licenses for the course software, and installation support will be provided by the Methods School.


Core Readings

Hayes, Andrew F. 2013. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-based Approach. New York, NY: Guilford Press.

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 105: 765-789.

Imai, Kosuke, Dustin Tingley, and Teppei Yamamoto. 2013. Experimental Designs for Identifying Causal Mechanisms. Journal of the Royal Statistical Society: Series A 176: 5-51.

MacKinnon, David P. 2008. Introduction to Statistical Mediation Analysis. New York, NY: Lawrence Erlbaum Associates.

Preacher, Kristopher J. 2015. Advances in Mediation Analysis: A Survey and Synthesis of New Developments. Annual Review of Psychology 66: 825-852.

Tingley, Dustin, Teppei Yamamoto, Kentaro Hirose, Luke Keele, and Kosuke Imai. 2014. mediation: R Package for Causal Mediation Analysis. Journal of Statistical Software 59: 1-38.


Suggested Readings

Ansolabehere, Stephen, James M. Snyder, and Charles Stewart. 2000. Old Voters, New Voters, and the Personal Vote: Using Redistricting to Measure the Incumbency Advantage. American Journal of Political Science 44: 17-34.

Baron, Reuben M., and David A. Kenny. 1986. The Moderator–mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of Personality and Social Psychology 51: 1173-1182.

Brader, Ted, Nicholas A. Valentino, and Elizabeth Suhay. 2008. What Triggers Public Opposition to Immigration? Anxiety, Group Cues, and Immigration Threat. American Journal of Political Science 52: 959-978.

Bullock, John G., and Shang E. Ha. 2011. Mediation Analysis Is Harder than It Looks. In: Druckman, James, N., Donald P. Green, James H. Kuklinski, and Arthur Lupia (eds.). Cambridge Handbook of Experimental Political Science. New York, NY: Cambridge University Press.

Chen, Chao, Yu Bai, and Rui Wang. 2019. Online Political Efficacy and Political Participation: A Mediation Analysis Based on the Evidence from Taiwan. New Media & Society: 1-30.

Cox, Gary W., and Jonathan N. Katz. 1996. Why Did the Incumbency Advantage in US House Elections Grow? American Journal of Political Science 40: 478-497.

Gadarian, Shana Kushner, and Bethany Albertson. 2014. Anxiety, Immigration, and the Search for Information. Political Psychology 35: 133-164.

George, Alexander L., and Andrew Bennett. 2005. Case Study and Theory Development in the Social Sciences. Boston, MA: MIT Press.

Holland, Paul W. 1986. Statistics and Causal Inference. Journal of the American Statistical Association 81: 945-960.

Huhe, Narisong, Min Tang, and Jie Chen. 2018. Creating Democratic Citizens: Political Effects of the Internet in China. Political Research Quarterly 71: 757-771.

Imai, Kosuke, Luke Keele, and Teppei Yamamoto. 2010. Identification, Inference and Sensitivity Analysis for Causal Mediation Effects. Statistical Science 25: 51-71.

Imai, Kosuke, Luke Keele, and Dustin Tingley. 2010. A General Approach to Causal Mediation Analysis. Psychological Methods 15: 309-334.

Keele, Luke, Dustin Tingley, and Teppei Yamamoto. 2015. Identifying Mechanisms Behind Policy Interventions via Causal Mediation Analysis. Journal of Policy Analysis and Management 34: 937-963.

Pearl, Judea. 2014. Interpretation and Identification of Causal Mediation. Psychological Methods 19: 459-481.

Rubin, Donald B. 1974. Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of educational Psychology 66: 688-701.

Zeitzoff, Thomas. 2014. Anger, Exposure to Violence, and Intragroup Conflict: A "Lab in the Field" Experiment in Southern Israel. Political Psychology 35: 309-335.


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