Predictive Analytics in Child Welfare Decision Tool


Predictive analytics is increasingly seen as a technology that can improve child welfare outcomes, turning hindsight into insight and insight into value. Predictive analytics can be defined as analysis that uses data, statistics, and algorithms to answer the question "Given past behavior, what is likely to happen in the future?" It is important to note that predictive analytics has the potential to produce benefits only when model results are used to intervene differentially based on the model output, leading to improvement on the outcome of interest.

This tool will guide the user through a series of criteria to help determine whether predictive analytics is an appropriate approach for the child welfare question you are considering. Before getting started, be prepared to walk through each of these sections with a specific question in mind such as, "Can we predict the number of children who will be reunited with families next year?" or "Can we predict which children in foster care are likely to experience a placement disruption in the next six months?" Reading and discussing the criteria below enables you to decide whether you have the building blocks to use predictive analytics for your particular question in child welfare. If not, explore the suggestions provided for addressing each item. Keep in mind that answering "Yes" to each criterion does not eliminate all risk for your project. Any "No" answer indicates that predictive analytics is not currently an appropriate approach. As you work through each criterion, click the ⓘ for more information about the issue and why it is important.

For a more detailed discussion on this topic, see Predictive Analytics in Child Welfare: An Introduction for Administrators and Policy Makers.

Data sufficiency

Is there sufficient, available quality data that is relevant to the chosen question?

  • Does the data capture attributes of the child and perpetrator environment that can explain the question?
  • Is there data on the prediction outcomes, both positive and negative?

Data quantity

Is there enough data, that is, an ample number of observations of the event you are trying to predict and the various circumstances that may contribute to the outcome, to provide an adequate base of information on which to build a model(s)?

Identified implementation strategy

Is there an effective implementation strategy identified for use once the predictive analytics have been completed?

  • Can the predictive model results be used in such a way that has a measurable, positive impact on the child welfare system?

Resource requirements

Can the predictive analytics efforts be completed at a cost that is projected to be less than the perceived benefit after implementation?

Stakeholder support

Is there enough approval – within the child welfare agency, the government at large, and among key stakeholders – to support the implementation of the predictive analytics effort? Is the agency prepared to be transparent about both the analytics process and the results?

Validity of the model

Is the modeling process rigorous and appropriate for the chosen question?

  • Does the model accurately represent the real world?
  • Do subject matter experts approve of the model results?

Accuracy of the model

To what extent does your model correctly predict the outcome of interest?

  • Are the false positive and false negative rates within the risk tolerance for the agency?

Precision of the model

Can the model reliably predict accurate results for multiple cases?

  • Does the model have enough consistency with its predictions to be implemented in the field?