text, our discussion aims to clarify the logic and contributions of studies of causality to the understanding of developmental processes and to interventions aimed at affecting these processes. Something close to a consensus has emerged within statistical science on the logic of causal inference: its definition, the conditions required for valid causal inference, and generalization of causal inferences. Appendix B discusses the statistical issues involved in defining and estimating causal effects. In the committee's view, this consensus has implications for all studies making causal comparisons, basic and applied, experimental and nonexperimental. Here we sketch the essential ideas in this emerging consensus and consider how these ideas can be applied to improving early childhood research. This focus is not intended to minimize the importance of other research strategies and goals. Research is most appropriately viewed as a sequential process, properly starting with exploratory observation, moving through correlational work aimed at tracing associations among variables of interest, to more rigorous designs permitting causal inference. Indeed, the richness of developmental science derives from the field's reliance on multiple methods of inquiry, and its greatest insights often emerge at the convergence of diverse strands of evidence.

We begin by considering causal inference in basic and applied developmental research. Basic research attempts to uncover fundamental processes of development and change, while applied research aims to help policy makers and practitioners evaluate practical efforts to improve children 's experiences and outcomes. We emphasize the importance of integrating basic and applied research in building a strong science of early childhood development. Insights from basic science are crucial in the design of practical programs, while the evaluation of programs can provide new evidence essential to basic science about casual connections. We then discuss the problem of generalizing from intervention studies to the populations of children, to the settings and personnel, and to the historical times and social contexts that might ultimately characterize a new program if its adoption became more widespread. Well-designed studies can answer important questions about the generalizability of a study result. Nevertheless, because strong generalizations typically can emerge only from a stream of related studies, we also discuss the importance of synthesizing evidence across multiple studies. Finally, we consider the particularly thorny issue of causal inference as it applies to growing children.

CAUSAL INFERENCE IN BASIC RESEARCH

The theory and evidence contained in this report are connected by chains of causal reasoning. We consider how prenatal and neonatal environments affect early brain development and behavior and how these early



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