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Causal Inference in Psychiatric Epidemiology.

There is no question more fundamental for observational epidemiology than that of causal inference. When, for practical or ethical reasons, experiments are impossible, how may we gain insight into the causal relationship between exposures and outcomes? This is the key question that Quinn et al1 seek to answer: does maternal smoking during pregnancy (SDP) cause offspring serious mental illness (SMI)? The problem of causation has a huge, fascinating, and occasionally bewildering literature with major contributions from philosophy, statistics, and epidemiology (see Susser,2 Woodward,3 and Illari and Russo4 for helpful introductions). Our focus here will be on the problem of confounding. As depicted in Figure 1A, we want to understand how much of the association between the exposure (here SDP) and the outcome (SMI) is the result of the direct causal path vs a result of the two groups of possible confounders: those measured and those not measured. The functional definition of a confounder is an external variable that predicts both the exposure and the outcome, and thus causes the two to correlate. The most common approach to the problem of confounding is multiple regression, where the outcome is predicted from the exposure and measured confounders. The hope is that the resulting exposure-outcome relationship will reflect causal effects. But this approach suffers from a major problem. Have you identified all the possible confounders? And, even if you know them, are they well measured in your sample? Figure 1B depicts a co-relative approach to the problems of confounders, used by Quinn et al.1 Here confounders are divided into familial and nonfamilial. As a rough definition, think of “familial” as confounding variables that are substantially correlated in siblings who grew up together. A strength of this design is that a large majority of human behavioral traits are correlated in relatives, often substantially.5 Think of nonfamilial confounders as experiences that make siblings different from one another in both risk for the exposure and the outcome. A major difference between Figure A and Figure B is that the familial confounders do not need to be measured nor does the researcher need to even be aware of their existence. Comparing the rates of the outcome in siblings who were vs were not exposed controls for the familial components of all confounders. Co-relative designs resemble randomized controlled trials in that they account for the effect of both known and unknown confounders. But there is a problem. The co-relative design does not work for nonfamilial confounders—those unique to individuals. An example may clarify this point: Consider a sibling pair discordant for impulsivity (the exposure) where the impulsive sibling has poor school performance while the unimpulsive sib does not. This pair seems to provide evidence for a causal effect of impulsivity on school achievement. But, what if the affected sibling had a serious head trauma at age 8 with subsequent impulsivity and poor school performance? Maybe the relationship is not causal but due to a nonfamilial confounder—head trauma. So, while very helpful, the co-relative design is no panacea for all problems of causal inference. Two more points about this design are noteworthy. First, it only controls fully for genetic confounding in monozygotic (MZ) twin pairs discordant for exposure. Finding enough such pairs, even in Scandinavian registries, can be difficult. For intrauterine exposures like SDP, furthermore, twins are always concordant. However, we have relatives that share different degrees of environmental exposures and genes such as cousins, half-siblings, full siblings, and MZ twins. These can be used to create a “dose-response” curve of increasing control for familial confounders. With such data, it is possible to fit a model to estimate results for discordant MZ twins from the other more common relative pairs.6 Second, the interpretation of the co-relative design is somewhat asymmetric. When, as in Quinn et al,1 the exposureoutcome relationship is substantially attenuated in coRelated article Figure 1. Approaches to Causal Inference