Sibling comparison designs have long been used to assess causal effects of exposures for which randomized studies are impossible and measurement of all relevant confounding is unobtainable. The idea is to utilize the fact that siblings often share a lot of unobserved variables. Therefore, it is proposed that in certain cases, comparing siblings is equivalent to comparing exchangeable individuals, which is the foundation for causal inference based on randomized controlled trials (RCTs). However, this intuition-and the publication of highly important sibling studies-vastly predate modern causal inference theory. Full causal descriptions of sibling comparison designs are essentially nonexistent, and therefore it is not clear exactly how or if we can interpret their estimated effects as causal. We fill this theoretical gap by proposing a counterfactual-based framework for sibling comparison designs. Moreover, we employ this framework to derive precise causal interpretations for three commonly used sibling model estimators stemming from fixed-effects ordinary least squares (OLS), conditional logistic regression, and stratified Cox regression. We establish that, for the latter two, the obtained effect parameter describes a causal effect on the full sibling group, not the individuals, and thus it does not correspond to the prevailing intuition from the RCT analogue. For fixed-effects OLS estimation, the parameter describes a causal effect on an individual, but may depend on an intervention on the whole sibling group. OLS estimation thus results in an estimator that can be given a simple causal interpretation that is similar, but not equal to, the RCT parallel. See video abstract at, http://links.lww.com/EDE/B618.