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Causal map showing the causal factor 'COVID-19' as well as factors one step downstream of it; simplified to show only the most frequent factors.
Source publication
What do the intended beneficiaries of international development programmes think about the causal drivers of change in their livelihoods and lives? Do their perceptions match up with the theories of change constructed by organizations trying to support them? This case study looks at an entrepreneurship programme aiming to economically empower rural...
Contexts in source publication
Context 1
... certain assumptions, it is possible to ask and answer questions such as, 'Which factor has the widest influence?' or 'Which factor leads to the most positive outcomes?'. Figure 1 gives an example of a simple causal map. This one arose from the study presented later in this chapter. ...
Context 2
... Covid-19 crisis (Fig. 1) and resulting measures affected many dimensions of the programme, in both its implementation and in the areas it aims to impact. The respondents were asked specifically about the changes resulting from Covid-19 in order to identify its impact and isolate it from changes prior to the crisis. Respondents reported how the situation ...
Citations
... We see the job of the causal mapper as being primarily to collect and accurately visualise evidence from different sources, often leaving it to others (or to themselves wearing a different hat) to draw conclusions about what doing so reveals about the real world. This second interpretative step goes beyond causal mapping per se (Copestake, 2021;Copestake et al., 2019a;Powell et al., 2023). ...
... Causal maps help us to assemble evidence for the causal processes at work in specified domains, including the influence of activities being evaluated. They can also help expose differences between the evidence given by different sources and differences between the analysed data and theories of change derived from other sources, including those officially espoused by the commissioner of the evaluation (Powell et al., 2023). The identification of differences in understanding can then feed into further enquiry, analysis and action concerning why people have different views, what the implications of this are and how these might be addressed. ...
Evaluators are interested in capturing how things causally influence one another. They are also interested in capturing how stakeholders think things causally influence one another. Causal mapping – the collection, coding and visualisation of interconnected causal claims – has been used widely for several decades across many disciplines for this purpose. It makes the provenance or source of such claims explicit and provides tools for gathering and dealing with this kind of data and for managing its Janus-like double-life: on the one hand, providing information about what people believe causes what, and on the other hand, preparing this information for possible evaluative judgements about what causes what. Specific reference to causal mapping in the evaluation literature is sparse, which we aim to redress here. In particular, the authors address the Janus dilemma by suggesting that causal maps can be understood neither as models of beliefs about causal pathways nor as models of causal pathways per se but as repositories of evidence for those pathways.
... Causal maps help us to assemble evidence for the causal processes at work in specified domains, including the influence of activities being evaluated. They can also help expose differences between the evidence given by different sources and differences between the analysed data and theories of change derived from other sources, including those officially espoused by the commissioner of the evaluation (Powell et al., 2023). The identification of differences in understanding can then feed into further enquiry, analysis and action concerning why people have different views, what the implications of this are and how these might be addressed. ...
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Evaluators are interested in capturing how things causally influence one another. They are also interested in capturing how stakeholders think things causally influence one another. Causal mapping, the collection, coding and visualisation of interconnected causal claims, has been used widely for several decades across many disciplines for this purpose. It makes the provenance or source of such claims explicit and provides tools for gathering and dealing with this kind of data, and for managing its Janus-like double-life: on the one hand providing information about what people believe causes what and on the other hand preparing this information for possible evaluative judgements about what actually causes what. Specific reference to causal mapping in the evaluation literature is sparse, which we aim to redress here. In particular we address the Janus dilemma by suggesting that causal maps can be understood neither as models of beliefs about causal pathways nor as models of causal pathways per se but as repositories of evidence for those pathways.</p
... We see the job of the causal mapper as being primarily to collect and accurately visualise evidence from different sources, often leaving it to others (or to themselves wearing a different hat) to draw conclusions about what doing so reveals about the real world. This second interpretative step goes beyond causal mapping per se (Copestake, 2020a;Copestake, Davies, et al., 2019;Powell et al., 2023). ...
... Causal maps help us to assemble evidence for the causal processes at work in specified domains, including the influence of activities being evaluated. They can also help expose differences between the evidence given by different sources, and differences between the analysed data and theories of change derived from other sources, including those officially espoused by the commissioner of the evaluation (Powell et al., 2023). The identification of differences in understanding can then feed into further enquiry, analysis and action concerning why people have different views, what the implications of this are, and how these might be addressed. ...
p>
Evaluators are interested in capturing how things causally influence one another. They are also interested in capturing how stakeholders think things causally influence one another. Causal mapping, the collection, coding and visualisation of interconnected causal claims, has been used widely for several decades across many disciplines for this purpose. It makes the provenance or source of such claims explicit and provides tools for gathering and dealing with this kind of data, and for managing its Janus-like double-life: on the one hand providing information about what people believe causes what and on the other hand preparing this information for possible evaluative judgements about what actually causes what. Specific reference to causal mapping in the evaluation literature is sparse, which we aim to redress here. In particular we address the Janus dilemma by suggesting that causal maps can be understood neither as models of beliefs about causal pathways nor as models of causal pathways per se but as repositories of evidence for those pathways.</p