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1.  Statistics and Causality: Separated to Reunite—Commentary on Bryan Dowd's “Separated at Birth” 
Health Services Research  2011;46(2):421-429.
PMCID: PMC3064911  PMID: 21371028
2.  Invited Commentary: Understanding Bias Amplification 
American Journal of Epidemiology  2011;174(11):1223-1227.
In choosing covariates for adjustment or inclusion in propensity score analysis, researchers must weigh the benefit of reducing confounding bias carried by those covariates against the risk of amplifying residual bias carried by unmeasured confounders. The latter is characteristic of covariates that act like instrumental variables—that is, variables that are more strongly associated with the exposure than with the outcome. In this issue of the Journal (Am J Epidemiol. 2011;174(11):1213–1222), Myers et al. compare the bias amplification of a near-instrumental variable with its bias-reducing potential and suggest that, in practice, the latter outweighs the former. The author of this commentary sheds broader light on this comparison by considering the cumulative effects of conditioning on multiple covariates and showing that bias amplification may build up at a faster rate than bias reduction. The author further derives a partial order on sets of covariates which reveals preference for conditioning on outcome-related, rather than exposure-related, confounders.
PMCID: PMC3224255  PMID: 22034488
bias (epidemiology); confounding factors (epidemiology); epidemiologic methods; instrumental variable; precision; simulation; variable selection
3.  Principal Stratification — a Goal or a Tool? 
Principal stratification has recently become a popular tool to address certain causal inference questions, particularly in dealing with post-randomization factors in randomized trials. Here, we analyze the conceptual basis for this framework and invite response to clarify the value of principal stratification in estimating causal effects of interest.
PMCID: PMC3083139  PMID: 21556288
causal inference; principal stratification; surrogate endpoints; direct effect; mediation
4.  An Introduction to Causal Inference* 
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.
PMCID: PMC2836213  PMID: 20305706
structural equation models; confounding; graphical methods; counterfactuals; causal effects; potential-outcome; mediation; policy evaluation; causes of effects

Results 1-4 (4)