Past exam of the psychological disorders: risk and resilience course of the University of Oxford / 2022 / 3 / Solution

A risk factor is a cause when an appropriate change in that factor would change the relevant psychological outcome, under specified conditions. Prediction and causation are different: a variable can forecast disorder because it is a marker, a consequence of emerging illness, or correlated with an unmeasured cause. Confidence comes from designs that address different alternative explanations and from convergence on a clearly defined exposure, population and outcome.

A cross-sectional study can establish an association and suggest hypotheses, but timing is ambiguous. A depressed respondent may recall more adversity, and family disadvantage may influence both exposure and symptoms. A case–control study is efficient for uncommon disorders but remains sensitive to recall, control selection and exposure misclassification. Adjusting for many measured variables is not automatically an improvement: controlling a consequence of exposure can block a real pathway, while conditioning on a common consequence of exposure and outcome can induce collider bias.

Prospective longitudinal studies improve temporal ordering. In Constance Hammen and colleagues' two-year follow-up, childhood-adversity histories predicted depressive reactions at lower subsequent psychological stress levels. Measuring events and symptoms over time made this more informative than asking affected people retrospectively why they became ill. Yet adversity was not randomized, and histories may be measured imperfectly. Temporal precedence is necessary for the proposed direction of influence, but does not remove familial liability, prior symptoms or unmeasured social circumstances as competing explanations.^[ref]

Stronger observational designs make their counterfactual comparison explicit. A natural experiment exploits an external change that alters exposure without being produced by the same individual characteristics that usually determine it. E. Jane Costello and colleagues observed children before and after an income supplement associated with a casino opening. Comparing developmental changes across differently affected families strengthened the social-causation interpretation for particular behavioral outcomes. It did not randomize poverty, and concurrent community changes or selection into an income-defined subgroup remain possible alternatives.^[ref]

A difference-in-differences analysis formalizes such reasoning as the exposed group's change minus the comparison group's change. Its causal interpretation requires a credible untreated trend comparison, no differential concurrent change that explains the effect, and careful attention to anticipation and changes in sample composition. Merely measuring before and after a policy is not sufficient. Examining pre-intervention trends can expose some problems but cannot prove that every counterfactual assumption is true.

A randomized controlled trial offers another route by breaking systematic baseline links between allocation and measured or unmeasured causes. The foster-care offer in the Bucharest Early Intervention Project is an example of ethically intervening to improve a harmful setting, rather than deliberately assigning children to initial deprivation. Under suitable follow-up and analysis, differences between allocated groups estimate the effect of offering that intervention. Nonadherence, placement changes and attrition still matter. An intention-to-treat analysis retains the allocation comparison; simply regrouping children by the care they eventually received can reintroduce selection.^[ref]

Even a successful trial does not identify the causal contribution of every component. A foster-care package changes several experiences simultaneously. A coping course may alter attention, expectations, peer contact and behavior. To identify a mediator, it is necessary to consider confounding of the mediator–outcome relationship and its temporal ordering, not only whether the treatment changes both mediator and outcome. Conversely, a null intervention can reflect inadequate delivery, insufficient contrast or the wrong developmental timing; it need not prove that the proposed risk factor is irrelevant under every possible intervention.

Genetically informed studies address another set of alternatives. Comparing differently exposed identical twins can reduce confounding by inherited sequence and shared family background. However, exposure differences can arise from nonshared antecedents that also affect disorder, and within-pair measurement error can be substantial. Family and adoption designs are similarly informative without making every remaining environmental association causal. Molecular associations can indicate inherited liability, but linkage disequilibrium and population stratification separate an associated marker from a demonstrated mechanism.

Mendelian randomization uses genetic variants as instruments for an exposure, requiring relevant instruments, independence from exposure–outcome confounders and an exclusion restriction. The design can be useful when its assumptions are credible. It is not a shortcut for every social risk factor: horizontal pleiotropy, parental genetic effects and ancestry structure can create misleading estimates. A genetic score associated with stressful experiences does not necessarily act only through those experiences. This is a methodological distinction, not evidence that any particular untested exposure is causal.

Animal experiments can manipulate mechanisms and timing more tightly. Karen J. Parker and colleagues randomized young squirrel monkeys to intermittent separation experiences or a comparison condition and later tested responses in a novel setting. This supports causal conclusions about that rearing contrast and those measured responses. It does not directly estimate the effect of childhood adversity on human psychiatric diagnoses: species, development, psychological stressor meaning and outcomes differ.^[ref]

The strongest conclusion is obtained when these designs converge despite their different weaknesses, with sensitivity analyses and transparent outcome definitions. A causal claim should specify the contrast it establishes and the alternatives it has not excluded. Prospective timing, externally driven exposure changes, randomization, within-family controls and mechanistic experiments together can provide much stronger evidence than any isolated risk correlation, while still leaving uncertainty about transport to another population or developmental stage.