Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

RE: reL Re: st: Interpreting mediation model sobel goodman test

From   "Ariel Linden, DrPH" <>
To   <>
Subject   RE: reL Re: st: Interpreting mediation model sobel goodman test
Date   Sun, 13 Nov 2011 11:13:54 -0500

Thank you, Cameron! I have read most of these papers, but there are some
that are new to me...

I would further suggest the following:

Hong, G. (2010b). Ratio of mediator probability weighting for estimating
natural direct and indirect effects. In 2010 proceedings of the American
Statistical Association, Biometrics Section (pp. 2401-2415). Alexandria, VA:
American Statistical Association.

Guanglei Hong, Jonah Deutsch, Heather Hill. Parametric and Non-Parametric
Weighting Methods for Mediation Analysis: An Application to the National
Evaluation of Welfare-to-Work Strategies. Proceedings of the American
Statistical Association, Biometrics Section. (In press)

Peterson, M. L., Sinisi, S. E., & van der Laan, M. J. (2006). Estimation of
direct causal effects. Epidemiology, 17(3), 276-284.


Date: Sat, 12 Nov 2011 15:12:05 -0500
From: Cameron McIntosh <>
Subject: RE: reL Re: st: Interpreting mediation model sobel goodman test

Hi Ariel,
John's response is part of the iceberg (not necessarily only the tip,
perhaps somewhere nearer the foundation) that is showing the deficiencies in
the traditional statistical approaches to mediation. There is a large
literature on causal mediation that you really should look into: 
Ten Have, T.R., & Joffe, M. (2010). A review of causal estimation of effects
in mediation analyses. Statistical Methods in Medical Research, Epub ahead
of print.
Small, D.S. (September 7, 2011). Mediation Analysis Without Sequential
Ignorability: Using Baseline Covariates Interacted with Random Assignment as
Instrumental Variables.Philadelphia, PA: Department of Statistics, The
Wharton School of the University of
Pearl, J. (2011a). The mediation formula: A guide to the assessment of
causal pathways in non-linear models. Tech. Rep. R-363. Los Angeles, CA:
Department of Computer Science, University of California. To appear in C.
Berzuini, P. Dawid, and L. Bernardinelli (Eds.), Causality: Statistical
Perspectives and Applications. Forthcoming, 2011.
Pearl, J. (2011b). The Causal Mediation Formula - A Guide to the Assessment
of Pathways and Mechanisms. Tech. Rep. R-363. Los Angeles, CA: Department of
Computer Science,University of California. Forthcoming, Preventive
Judea Pearl and possibly others. (November, 2011).  Interpretable conditions
for identifying natural direct effects. Technical Report R-389. Los Angeles,
CA: Department of Computer Science,
Gerring, J. (2010). Causal Mechanisms: Yes, But. Comparative Political
Studies, 43(11),
Bullock J.G., Green, D.P., & Han, S.E. (2010). Yes, but what's the
mechanism? (don't expect an easy answer). Journal of Personality and Social
Psychology, 98(4),
Albert, J.M., & Nelson, S. (2011). Generalized Causal Mediation Analysis.
Biometrics, 67(3),
Shpitser, I., & VanderWeele, T.J. (2011). A Complete Graphical Criterion for
the Adjustment Formula in Mediation Analysis. The International Journal of
Biostatistics, 7(1), Article 16.
VanderWeele, T.J.,  & Vansteelandt, S. (2009). Conceptual issues concerning
mediation, interventions and composition. Statistics and Its Interface, 2,
VanderWeele, T.J. (2011). Controlled Direct and Mediated Effects:
Definition, Identification and Bounds. Scandinavian Journal of Statistics,
38(3), 551-563.
Glynn, A.N. (May 11, 2011). The Product and Difference Fallacies for
Indirect Effects.
Glynn, A.N. (July 14, 2008).Estimating and Bounding Mechanism Specific
Causal Effects
Imai, K., Keele, L., Tingley, D., & Yamamoto, T. (2011). Unpacking the Black
Box of Causality: Learning about Causal Mechanisms from Experimental and
Observational Studies. Forthcoming in American Political Science
Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal
mediation analysis. Psychological Methods, 15(4),
Imai, K., Keele, L., & Yamamoto, T. (2010a). Identification, Inference and
Sensitivity Analysis for Causal Mediation Effects. Statistical Science,
25(1), 51-71.
Imai, K., Keele, L., & Yamamoto, T. (2010b). Replication data for:
Identi?cation, inference, and sensitivity analysis for causal mediation
effects. Available at
Imai, K., Keele, L., & Tingley, D., & Yamamoto, T. (2010). Causal Mediation
Analysis Using R. In H. D. Vinod (Ed.), Advances in Social Science Research
Using R (pp. 129-154). New York: Springer (Lecture Notes in
Keele, L., Tingley, D., Yamamoto, T., & Imai, K. (August 21, 2011). Package
'mediation': R Package for Causal Mediation Analysis, Version
Tingley, D. (June 18, 2011). MEDIATION: Stata module for causal mediation
analysis and sensitivity
Imai, K., Tingley, D., & Yamamoto, T. (Draft of August 10, 2011).
Experimental designs for identifying causal
Imai, K., & Yamamoto, T. (Draft of June 29, 2011). Sensitivity analysis for
causal mediation effects under alternative exogeneity assumptions.

*   For searches and help try:

© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index