Correlational studies have played a major role in building our cumulative knowledge on child development. Yet as a result, we often have difficulty making causal inferences. The concern is selection effects: When children have not been randomly assigned to conditions, pre-existing biological, psycho- logical, or social factors may bias correlations. In this article, we draw attention to sensitivity analyses, statis- tical techniques for estimating the robustness or fragility of results in light of potential selection effects. We high- light the coefficient of proportionality method recently developed by Oster (2019), which does not require assumptions about the number of omitted selection vari- ables. The coefficient of proportionality provides an indication of how large the impact of unobserved selec- tion factors would need to be—relative to observed covariates—to nullify a result. We offer two empirical examples to demonstrate the value of this method com- pared with other approaches used by child development researchers.