Predicting What Will Happen When You Intervene

Area of Science:

• Social Work Research
• Causal Inference

Background:

• Social work practice often requires predicting intervention outcomes for specific clients.
• Existing research may not fully address the need for case-specific predictive modeling.

Purpose of the Study:

• To provide social workers with practical rules of thumb for developing predictive models.
• To enable 'ex ante case-specific causal models' for before-the-fact prediction of intervention effects.
• To focus on predicting outcomes for individual clients in their real-world contexts.

Main Methods:

• Developing rules of thumb for constructing causal models.
• Integrating general and local knowledge for case-specific predictions.
• Applying principles similar to post facto realist evaluations.

Main Results:

• The paper outlines a methodology for creating predictive models tailored to individual cases.
• It emphasizes the 'ex ante' (before-the-fact) nature of these predictions.
• The models aim to trace causal processes influencing client outcomes.

Conclusions:

• The proposed approach offers a framework for enhancing predictive accuracy in social work practice.
• These models, while not perfectly reliable, can guide interventions for specific clients.
• The methodology supports a more nuanced understanding of causality in social work case studies.