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Predicting What Will Happen When You Intervene.

Nancy Cartwright¹, Jeremy Hardie²

¹Durham University, University of California, San Diego, San Diego, USA.

Clinical Social Work Journal

|August 29, 2017

Summary

This study introduces practical guidelines for social workers to build predictive models for client interventions. These ex ante case-specific causal models aid in forecasting intervention effects for individual clients.

Keywords:

Causation Deliberation Ex ante Prediction Support factor

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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.