Understanding the extent to which an intervention ‘works’ can provide compelling evidence to decision-makers, although without an accompanying explanation of how an intervention works, this evidence can be difficult to apply in other settings, ultimately impeding its usefulness in making judicious and evidence-informed decisions.
In this paper, we describe causal chain analysis as involving the development of a logic model, which outlines graphically a hypothesis of how an intervention leads to a change in an outcome. This logic model is then used to anchor subsequent decisions in the systematic review process, including decisions on synthesis.
In this paper, we outline the steps taken in building a logic model, which usually consists of a series of boxes depicting intervention components and processes, outputs, and outcomes with arrows depicting connecting relationships. The nature of these connecting relationships and their basis in causality are considered, through a focus on complex causal relationships and the way in which contextual factors about the intervention setting or population may moderate these. We also explore the way in which specific combinations of intervention components may lead to successful interventions. Evidence synthesis techniques are discussed in the context of causal chain analysis, and their usefulness in exploring different parts of the causal chain or different types of relationship. The approaches outlined in this paper aim to assist systematic reviewers in producing findings that are useful to decision-makers and practitioners, and in turn, help to confirm existing theories or develop entirely new ways of understanding how interventions effect change.

Explores the application of causal chain analysis for systemic reviews. Includes an account of the causal nature of the connecting relationships in a ToC. Evidence synthesis techniques are discussed in the context of causal chain analysis.

Causal Chain Analysis in Systematic Reviews of International Development Interventions

Impact investing is becoming one of the largest forces in driving social and environmental change globally. However, how one defines, measures and communicates this impact is not well defined or consistently implemented. This can prevent investors from making well-informed decisions and allows for ‘impact washing’. The evaluation community has many tools that could be adapted and used in the world of impact investing. Theories of change allow for the better communication of impact, identification of indicators to be measured and critical interrogation of logic. The attributes of theories of change could assist in steering the growing force of impact investing towards gathering more investment and achieving greater impact. This paper is exploratory and examines the development and use of theories of change as a tool for impact investing and seeks to identify the benefits of the tool. We qualitatively review three case studies of organisations that have implemented theories of change and identify common key themes. We find that theories of change are a useful tool for the communication of impact, identification of indicators to be measured and for the critical interrogation of logic. However, theories of change do not provide a panacea to the impact challenge; the need to rigorously measure impact is not fulfilled by merely identifying what needs to be measured. Regardless, the use of theories of change adds an advantage in a space where others have not gone to the same length to show their commitment to driving change.

The authors look at three case studies of how ToCs help impact investing move forward.

Evaluative tools in impact investing: Three case studies on the use of theories of change

Evaluating systems change is HARD. But impact models can be an enormously powerful tool to help philanthropy make sense of data, develop strategies, and communicate their theory of change more clearly.
In this blog post six models are discussed of how impact may come about in reference to the status quo, i.e. how the outcomes would have been different when the intervention would not have been there:
<ol>
<li>Transformative</li>
<li>Proactive</li>
<li>Opportunistic</li>
<li>Stabilizing</li>
<li>Preventative</li>
<li>Palliative</li>
</ol>
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<video class="wp-video-shortcode" id="video-4170-1" width="750" height="375" preload="metadata" controls="controls"><source type="video/mp4" src="https://content.changeroo.com/wp-content/uploads/Academy/2018/09/democracyfund_impact_eevaluation.mp4?_=1" /><a href="https://content.changeroo.com/wp-content/uploads/Academy/2018/09/democracyfund_impact_eevaluation.mp4">https://content.changeroo.com/wp-content/uploads/Academy/2018/09/democracyfund_impact_eevaluation.mp4</a></video></div>

Evaluating systems change is HARD. But impact models can be an enormously powerful tool to help philanthropy make sense of data, develop strategies, and communicate their theory of change more clearly.