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This article presents an artificial intelligence-assisted causal mapping pipeline for gathering and analysing stakeholder perspectives at scale. Evidence relevant to constructing a programme theory, as well as evidence for the causal influences flowing through it, are both collected at the same time, without the evaluator needing to possess a prior theory. The method uses an artificial intelligence interviewer to conduct interviews, automated coding to identify causal claims in the...
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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 i...
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Systems approaches are currently being advocated and implemented to address complex challenges in Public Health. These approaches work by bringing multi-sectoral stakeholders together to develop a collective understanding of the system, and then to identify places where they can leverage change across the system. Systems approaches are unpredictable, where cause-and-effect cannot always be disentangled, and unintended consequences – positive and negative – frequently arise. Evaluating such...
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The investigation of causal mechanisms has the capacity to provide donors and implementing institutions with a greater understanding of people's reasoning and reactions as they work with interventions. This chapter contributes to the literature by identifying behavioral mechanisms generated through engagement with climate-resilient agriculture interventions within a larger livelihood project in Ethiopia. It works through the steps that enabled the study to unpack the black box between the...
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This chapter argues that the credibility of causal mechanisms can be greatly increased by formulating them as statements that are both empirically falsifiable and empirically confirmable. Whether statements can be so depends on the potential availability of the relevant evidence (e.g., no evidence exists that can prove or disprove the existence of God, but good quality evidence is potentially available in many other cases). The Bayes formula can be used to measure the extent to which a given...
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Applied agricultural research institutes play different roles in complex agricultural innovation processes, contributing to them with other actors. To foster learning and usable knowledge on how research actions influence such lasting innovation processes, there is a need to identify the causal mechanisms linking these actions and the effects of the changes they enable. A participatory, theory-driven, ex-post evaluation method, ImpresS, was developed by the French Agricultural Research...
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Realist evaluation has, over the past two decades, become a widely used approach in evaluation. The cornerstone of realist evaluation is to answer the question: What works, for whom, under what circumstances, and why. This is accomplished by explicating the causal mechanisms that, within a particular context, generate the outcomes of interest. Despite the central role of mechanisms in realist evaluation, systematic knowledge about how the term mechanism is conceptualized and operationalized...
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Experimental evaluations—especially when grounded in theory-based impact evaluation—can provide insights into the mechanisms that generate program impacts. This chapter details variants of experimental evaluation designs and also analytic strategies that leverage experimental evaluation data to learn about causal mechanisms. The design variants are poised to illuminate causal mechanisms related to program implementation and the contribution of selected components of multifaceted programs....
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This chapter explores the use of mechanisms within the realist evaluation of the Building Capacity to Use Research Evidence (BCURE) program, a £15.7 million initiative aiming to improve the use of evidence in decision-making in low and middle-income countries. The evaluation was commissioned to establish not just whether BCURE worked but also how and why capacity building can contribute to increased use of evidence in policymaking in the very different contexts in which the program operated....
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Over the past decade, the field of development evaluation has seen a renewed interest in methodological approaches that can answer compelling causal questions about what works, for whom, and why. Development evaluators have notably started to experiment with Bayesian Process Tracing to unpack, test, and enhance their comprehension of causal mechanisms triggered by development interventions. This chapter conveys one such experience of applying Bayesian Process Tracing to the study of citizen...
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Realist evaluation is an approach with a strong emphasis on causal mechanisms and the context in which they are triggered. However, recent reviews of published realist evaluations show that context is often understudied. This is problematic, as a thorough understanding of the relationship between context and causal mechanisms is crucial in assisting policymakers to make appropriate and targeted decisions that improve the intervention. Therefore, we set out to test whether combining realist...
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Despite increased discussions in the community and a common understanding about the virtue of mechanism-based explanation, little is known about the true benefits and challenges of applying causal mechanism analysis in practice. This chapter aims to introduce the reader to the topic of causal mechanisms and synthesize significant findings on this special issue. It begins by laying out definitions and concepts of causal mechanisms in evaluation literature and proposes a two-way classification...
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MEL4 Adaptive Management
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