Over at the Structural Equation Modeling discussion listserve (SEMNET), participants lately have recommended several recent articles and resources on causal inference (and related topics) with non-experimental (correlational) research designs. For benefit of the larger research community, I have listed these materials below.

I've looked over some of these articles and they seem to vary in the prior training assumed. Some would seem accessible for social scientists without elaborate mathematical training, whereas others refer extensively to more sophisticated math (e.g., matrix algebra). The Antonakis et al. piece, in particular, appears to provide a (mostly) non-technical overview.

Three specific topics are covered in many of the articles:

I am particularly impressed by the range of academic discliplines from which these articles arise. Thanks to those who contributed these items to SEMNET!

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Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations.

*The Leadership Quarterly, 21,*1086-1120.

Austin, P.C. (2011). A tutorial and case study in propensity score analysis: An application to estimating the effect of in-hospital smoking cessation counseling on mortality.

*Multivariate Behavioral Research, 46*, 119-151.

Beguin, J., Pothier, D., & Côté, S.D. (2011). Deer browsing and soil disturbance induce cascading effects on plant communities: a multilevel path analysis.

*Ecological Applications, 21*, 439–451.

Bollen, K.A., Kirby, J.B., Curran, P.J., Paxton, P.M., & Chen, F. (2007). Latent variable models under misspecification: Two-Stage Least Squares (2SLS) and Maximum Likelihood (ML) estimators.

*Sociological Methods & Research, 36*, 48-86.

Bollen, K.A., & Bauer, D.J. (2004). Automating the selection of model-implied instrumental variables.

*Sociological Methods & Research, 32,*425-452.

Bollen, K.A., & Maydeu-Olivares, A. (2007). A polychoric instrumental variable (PIV) estimator for structural equation models with categorical variables.

*Psychometrika, 72,*309-326.

Clarke, K. (2005). The Phantom Menace: Omitted variable bias in econometric research.

*Conflict Management and Peace Science, 22*, 341-352.

Clarke, K. (2009). Return of the Phantom Menace: Omitted variable bias in econometric research.

*Conflict Management and Peace Science, 26*, 46-66.

Coffman, D.L. (2011). Estimating causal effects in mediation analysis using propensity scores.

*Structural Equation Modeling, 18*, 357-369.

Freedman, D.A., Collier, D., Sekhon, J.S., & Stark, P.B. (Eds.). (2009).

*Statistical models and causal inference: A dialogue with the social sciences.*Cambridge University Press. ISBN: 978-0521123909.

Frosch, C.A., & Johnson-Laird, P.N. (2011). Is everyday causation deterministic or probabilistic?

*Acta Psychologica, 137*, 280-291.

Hancock, G. R., & Harring, J. R. (2011, May). Using phantom variables in structural equation modeling to assess model sensitivity to external misspecification. Paper presented at the Modern Modeling Methods conference, Storrs, CT. (Hancock webpage to request copy.)

Hoshino, T. (2008). A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm.

*Computational Statistics & Data Analysis, 52*, 1413-1429.

Kirby, J.B., & Bollen, K.A. (2009). Using instrumental variable (IV) tests to evaluate model specification in latent variable structural equation models.

*Sociological Methodology, 39,*327–355. (Public copy)

Mahoney, J. (2008). Toward a unified theory of causality.

*Comparative Political Studies, 41*, 412-436.

Markus, K.A. (2011). Mulaik on atomism, contraposition and causation.

*Quality and Quantity*. Online First (subscription needed), http://www.springerlink.com/content/r754405614228w0v/

Markus, K.A. (2011). Real causes and ideal manipulations: Pearl's theory of causal inference from the point of view of psychological resarch methods. In P. McKay Illari, F. Russo & J. Williamson (Eds.),

*Causality in the sciences*(pp. 240-269). Oxford, UK: Oxford University Press. (Errata)

Pearl, J. (2010). On a class of bias-amplifying variables that endanger effect estimates. Technical Report R-356. In P. Grunwald & P. Spirtes (Eds.), Proceedings of UAI, 417-424. Corvallis, OR: AUAI.

Pearl, J. (2011, August). The causal foundations of structural equation modeling. UCLA Cognitive Systems Laboratory, Technical Report (R-370), http://ftp.cs.ucla.edu/pub/stat_ser/r370.pdf. Chapter for R. H. Hoyle (Ed.),

*Handbook of structural equation modeling*. New York: Guilford Press.

Shadish, W.R., & Steiner, P.M. (2010). A primer on propensity score analysis.

*Newborn & Infant Nursing Review, 10*, 19-26.

Shipley, B. (2009). Confirmatory path analysis in a generalized multilevel context.

*Ecology, 90*, 363-368.

Shipley, B. The Causal Toolbox: A collection of programs for testing or exploring causal relationships [website]. http://pages.usherbrooke.ca/jshipley/recherche/book.htm

Spector, P.E., & Brannick, M.T. (2011). Methodological urban legends: The misuse of statistical control variables.

*Organizational Research Methods, 14*, 287-305.

Steiner, P.M., Cook, T.D., Shadish, W.R., & Clark, M.H. (2010). The importance of covariate selection in controlling for selection bias in observational studies.

*Psychological Methods, 15*, 250-267.

Thoemmes, F.J., & Kim, E.S. (2011). A systematic review of propensity score methods in the social sciences.

*Multivariate Behavioral Research, 46*, 90-118.

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