Estimating causal effects in mediation analysis using propensity scores

Structural Equation Modeling

Volumn 18, Issue 3, 2011, Pages 357-369

  Coffman, Donna L ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Causal inference; Mediation; Propensity scores

Indexed keywords

EID: 79956357402     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705511.2011.582001     Document Type: Article

References (37)

1. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
2. Cole, D. A., & Maxwell, S. E. (2003). Testing mediation models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558-577.
3. Collins, L. M., Graham, J. W., & Flaherty, B. (1998). An alternative framework for defining mediation. Multivariate Behavioral Research, 33, 295-312.
4. Frangakis, C. E., & Rubin, D. B. (2002). Principal stratification in causal inference. Biometrics, 58, 20-29.
5. Gallop, R., Small, D. S., Lin, J. Y., Elliot, M. R., Joffe, M., & Ten Have, T. R. (2009). Mediation analysis with principal stratification. Statistics in Medicine, 28, 1108-1130.
6. Hirano, K., & Imbens, G. W. (2004). The propensity score with continuous treatments. In A. Gelman & X.-L. Meng (Eds.), Applied Bayesian modeling and causal inference from incomplete-data perspectives (pp. 73-84). Hoboken, NJ: Wiley.
7. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945-970.
8. Imai, K., & van Dyk, D. A. (2004). Causal inference with general treatment regimes: Generalizing the propensity score. Journal of the American Statistical Association, 99, 854-866.
9. Jo, B. (2008). Causal inference in randomized experiments with mediational processes. Psychological Methods, 13, 314-336.
10. Kang, J., & Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22(4), 523-539.
11. Lee, B. K., Lessler, J., & Stuart, E. A. (2010). Improving propensity score weighting using machine learning. Statistics in Medicine, 29, 337-346.
12. Little, R. J. A., & Rubin, D. B. (2000). Causal effects in clinical and epidemiological studies via potential outcomes: Concepts and analytical approaches. Annual Review of Public Health, 21, 121-145.
13. Liu, C. (2004). Robit regression: A simple robust alternative to logistic and probit regression. In A. Gelman & X.-L. Meng (Eds.), Applied Bayesian modeling and causal inference from incomplete-data perspectives (pp. 227-238). Hoboken, NJ: Wiley.
14. Luellen, J. K., Shadish, W. R., & Clark, M. H. (2005). Propensity scores: An introduction and experimental test. Evaluation Review, 29, 530-558.
15. Lynch, K. G., Kerry, M., Gallop, R., & Ten Have, T. R. (2008). Causal mediation analyses for randomized trials. Health Services Outcomes Research Methodology, 8, 57-76.
16. MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593-614.
17. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test the significance of the intervening variable effect. Psychological Methods, 7, 83-104.
18. MacKinnon, D. P., Taborga, M. P., & Morgan-Lopez, A. A. (2002). Mediation designs for tobacco prevention research. Drug and Alcohol Dependence, 68, S69-S83.
19. McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9, 403-425.
20. Robins, J. M., & Greenland, S. (1992). Identifiability and exchangeability of direct and indirect effects. Epidemiology, 3, 143-155.
21. Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1995). Analysis of semiparametric regressionmodels for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90, 106-121.
22. Rosenbaum, P. R. (1984). The consequences of adjustment for a concomitant variable that has been affected by the treatment. Journal of the Royal Statistical Society, 147, 656-666.
23. Rosenbaum, P. R. (2002). Observational studies (2nd ed.). New York, NY: Springer.
24. Rosenbaum, P. R., & Rubin, D. B. (1983a). Assessing sensitivity to an unobserved covariate in an observational study with binary outcome. Journal of the Royal Statistical Society, 45, 212-218.
25. Rosenbaum, P. R., & Rubin, D. B. (1983b). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
26. Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79, 516-524.
27. Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician, 39, 33-38.
28. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688-701.
29. Rubin, D. B. (2004). Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics, 31, 161-170.
30. Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322-331.
31. Schafer, J. L., & Kang, J. (2008). Average causal effects from non-randomized studies: A practical guide and simulated example. Psychological Methods, 13(4), 279-313.
32. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422-445.
33. Sobel, M. E. (1982). Asymptotic intervals for indirect effects in structural equations models. In S. Leinhart (Ed.), Sociological methodology (pp. 290-312). San Francisco, CA: Jossey-Bass.
34. Sobel, M. E. (2080). Identification of causal parameters in randomized studies with mediating variables. Journal of Educational and Behavioral Statistics, 33, 230-251.
35. Ten Have, T. R., Joffe, M., Lynch, K., Maisto, S., Brown, G., & Beck, A. (2007). Causal mediation analysis with rank-preserving models. Biometrics, 63, 926-934.
36. VanderWeele, T. (2006). The use of propensity score methods in psychiatric research. International Journal of Methods in Psychiatric Research, 15, 95-103.
37. Winship, C., & Morgan, S. L. (1999). The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659-706.