Although efforts have been made to study the relationship between particular childhood experiences and outcomes for children in order to understand the causal impact of these experiences, the overwhelming majority of such efforts have relied upon observational data. Regression analysis on the basis of this data has been employed by researchers in an effort to demonstrate a causal relationship between particular childhood activities/experiences and childhood outcomes. However, the enormous complexity of childrearing and family dynamics should give policymakers pause before accepting claims that all relevant variables can be identified and controlled for through this sort of analysis.
Since random assignment experiments are extremely difficult to perform in areas related to child development due to ethical constraints, researchers in the area of child policy often attempt to find “natural experiments” that are produced by particular events, such as policy changes in a particular jurisdiction. Studies that employ this sort of “quasi-experimental” design are often able to provide more convincing evidence of causal links between childhood experiences and developmental outcomes than studies that rely solely on observational data.
In the absence of evidence drawn from natural experiments or quasi-experimental designs, policymakers should exercise caution when presented with observational studies that purport to demonstrate that particular policies improve child development outcomes. Due to the complexity of the phenomena being studied, the potential for omitted variable bias should always be recognized, even when great care is taken to identify potentially confounding variables. Because of this weakness of observational studies, and the ethical constraints that often make random control experiments difficult in this area of policy, it is often extremely difficult to identify the most important determinants of child development outcomes and devise public policy accordingly.