Child welfare and the challenge of causal inference

Causal inference refers to the assessment of cause and effect relationships in observational data—i.e., in situations where random assignment is impossible or impractical. Choices involving children in the child welfare system evoke core elements of causal inference—manipulation and the counterfactual. How would a child's circumstances differ if we changed her environment? This article begins with one mathematical approach to framing causal inference, the potential outcomes framework. This methodology is a cornerstone of newer approaches to causal inference. This framework makes clear the identification problem inherent in causal inference and highlights a key assumption often used to identify the model (ignorability or no unobserved confounding). The article then outlines the various approaches to causal inference and organizes them around whether they assume ignorability as well as other key features of each approach. The article then provides guidelines for producing good causal inference. These guidelines emerge from a review of methodological literature as broad ranging as epidemiology, statistics, economics, and policy analysis. These steps will be illustrated using an example from child welfare. The article concludes with suggestions for how the field could apply these newer methods.