Figure 3 - uploaded by Fiona Remnant
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Causal map showing causal factors which include the words 'gender' or 'women' or 'relationships' as well as factors one step upstream or downstream of them, simplified to show only the most frequent factors and links.
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
... searches were made once the whole text had been analysed, for example, on gender, as shown in Figure 3. Some women reported positive gender-related changes within the household in the form of increased joint decision-making and a more equal distribution of the workload. ...
Context 2
... a cause for change in farming income experienced by the beneficiaries. This can be explained by the fact that this component had not yet been implemented by the programme staff. The causal maps from the study also reveal a different sequence in the programme ToC's expected causal mechanisms regarding gender equality and women's empowerment (see Fig. 3). The study identified four key drivers of change: joint decision-making, joint labour, better community relationships, and women occupying leadership positions. Some of these were articulated as (highest-level) outcomes in the programme ToC, therefore not articulating the follow-on positive impacts, including improvement in household ...
Context 3
... positions. Some of these were articulated as (highest-level) outcomes in the programme ToC, therefore not articulating the follow-on positive impacts, including improvement in household relationships and reduced workloads for women. Joint decision-making is not just an outcome but is shown in some cases to be part of a longer causal pathway (see Fig. 3) which reinforces further positive outcomes which were not explicitly mentioned in the programme ToC: better community relationships, collective working opportunities and men being more involved in household tasks. This type of causal mapping elaborated further on the causal mechanisms taking place, going beyond the programme ToC, ...
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