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Current official diagnostic explainer, used only to corroborate the DSM-5 qualifying-exposure and full-syndrome requirements already available before the exam. The served page also discusses later revision metadata; it is not presented as a pre-2022 research publication.

Residual exposure stratification subtracts the genetically predicted component of an exposure before forming exposure strata. Under a suitable additive first-stage model, this avoids directly conditioning on the component caused by the instrument and can reduce the resulting collider bias.

Reverse causality occurs when an apparent exposure-outcome association is partly or wholly generated by the outcome, or by an early stage of it, affecting the measured exposure.

Risk compensation is a possible behavioral response to a prevention intervention: feeling better protected can reduce vigilance or increase exposure to hazards, partially offsetting the intervention's direct protective effect. It is a hypothesis to investigate, not a conclusion established merely by an unfavorable treatment comparison. A randomized controlled trial of the overall package estimates its net assignment effect, which can include both physical protection and behavioral changes.

Selection bias occurs when inclusion in the analyzed sample depends on variables related to the exposure and outcome, making the observed association differ from its target-population counterpart.

Serum calcium is the concentration of calcium in blood serum. It can be an exposure in an observational study or Mendelian randomization, but genetically changing it need not reproduce the effect of changing dietary calcium intake.

For binary exposure $A$, the total causal effect on an additive scale is $\mathbb{E}[Y(1)-Y(0)]$. With mediator potential outcome $M(a)$ and nested outcome $Y(a,m)$, consistency gives $Y(a)=Y(a,M(a))$.

Two-sample Mendelian randomization combines genetic variant associations with an exposure estimated in one sample and their associations with an outcome estimated in another. It requires the instrumental variable assumptions and compatible associations across the populations; sample overlap can change the behaviour of weak instruments.

For a binary instrumental variable $Z$, exposure $X$, and outcome $Y$, the Wald estimator is the ratio of the instrument's effect on the outcome to its effect on the exposure:Under instrumental-variable assumptions it estimates a causal effect.

A weak instrument has only a small association with the endogenous exposure, making instrumental-variable estimators imprecise and potentially badly biased in finite samples.