Abstract

Causal mediation analysis is widely used across many disciplines to investigate possible causal mechanisms. Such an analysis allows researchers to explore causal pathways, going beyond the estimation of simple causal effects. Recently, Imai, Keele and Yamamoto (2008) and Imai, Keele, and Tingley (2009) developed general algorithms to estimate causal mediation effects with the variety of data types that are often encountered in practice. The new algorithms can estimate causal mediation effects for linear and nonlinear relationships, with parametric and nonparametric models, with continuous and discrete mediators, and various types of outcome variables. In this paper, we show how to implement these algorithms in the statistical computing language R . Our easy-to-use software, mediation, takes advantage of the object-oriented programming nature of the R language and allows researchers to estimate causal mediation effects in a straightforward manner. Finally, mediation also implements sensitivity analyses which can be used to formally assess the robustness of findings to the potential violations of the key identifying assumption. After describing the basic structure of the software, we illustrate its use with several empirical examples.

Software

mediation: R Package for Causal Mediation Analysis — CRAN / GitHub

Related Papers

Imai, Kosuke, and Zhichao Jiang (2020). ``Identification and Sensitivity Analysis of Contagion Effects in Randomized Placebo-Controlled Trials.'' Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 183, No. 4 (October), pp. 1637-1657.

Imai, Kosuke, Luke Keele, Dustin Tingley, and Teppei Yamamoto (2014). ``Comment on Pearl: Practical Implications of Theoretical Results for Causal Mediation Analysis.'' Psychological Methods, Vol. 19, No. 4 (December), 482-487.

Tingley, Dustin, Teppei Yamamoto, Luke Keele, and Kosuke Imai (2014). ``mediation: R Package for Causal Mediation Analysis.'' Journal of Statistical Software, Vol. 59, No. 5 (August), pp. 1-38.

Imai, Kosuke and Teppei Yamamoto (2013). ``Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing Experiments.'' Political Analysis, Vol. 21, No. 2 (Spring), pp. 141-171.

Imai, Kosuke, Dustin Tingley, and Teppei Yamamoto (2013). ``Experimental Designs for Identifying Causal Mechanisms.'' (with discussions) Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 176, No. 1 (January), pp. 5-51. Read before the Royal Statistical Society in March, 2012.

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. Reprinted in Advances in Political Methodology, R. Franzese, Jr. ed., Edward Elger, 2017.

Imai, Kosuke, Luke Keele, and Dustin Tingley (2010). ``A General Approach to Causal Mediation Analysis.'' Psychological Methods, Vol. 15, No. 4 (December), pp. 309-334.

Imai, Kosuke, Luke Keele, and Teppei Yamamoto (2010). ``Identification, Inference, and Sensitivity Analysis for Causal Mediation Effects.'' Statistical Science, Vol. 25, No. 1 (February), pp. 51-71.

Chan, K.C.G, K. Imai, S.C.P. Yam, Z. Zhang ``Efficient Nonparametric Estimation of Causal Mediation Effects.''