Although causal evidence synthesis is critical for the policy sciences—whether it be analysis for policy or analysis of policy—its repeatable, systematic, and transparent execution remains challenging due to the growing volume, variety, and velocity of policy-relevant evidence generation as well as the complex web of relationships within which policies are usually situated. To address these shortcomings, we develop a novel, semi-automated approach to synthesizing causal evidence from policy-relevant documents. Specifically, we propose the use of natural language processing (NLP) for the extraction of causal evidence and subsequent homogenization of the text; causal mapping for the collation, visualization, and summarization of complex interdependencies within the policy system; and graph analytics for further investigation of the structure and dynamics of the causal map. We illustrate this approach by applying it to a collection of 28 articles on the emissions trading scheme (ETS), a policy instrument of increasing importance for climate change mitigation. In all, we find 300 variables and 284 cause-effect pairs in our input dataset (consisting of 4524 sentences), which are reduced to 70 unique variables and 119 cause-effect pairs after homogenization. We create a causal map depicting these relationships and analyze it to demonstrate the perspectives and policy-relevant insights that can be obtained. We compare these with select manually conducted, previous meta-reviews of the policy instrument, and find them to be not only broadly consistent but also complementary. We conclude that, despite remaining limitations, this approach can help synthesize causal evidence for policy analysis, policy making, and policy research.

尽管因果证据合成对于政策科学至关重要（无论是政策分析还是政策分析），但由于政策相关证据生成的数量、种类和速度不断增长，其可重复、系统和透明的执行仍然具有挑战性作为政策通常所处的复杂关系网络。为了解决这些缺点，我们开发了一种新颖的半自动化方法来综合政策相关文件中的因果证据。具体来说，我们建议使用自然语言处理（NLP）来提取因果证据并随后对文本进行同质化；用于对政策系统内复杂的相互依赖关系进行整理、可视化和总结的因果映射；和图形分析，以进一步研究因果图的结构和动态。我们通过将其应用于有关排放交易计划 (ETS) 的 28 篇文章集来说明这种方法，排放交易计划是一种对于缓解气候变化日益重要的政策工具。总之，我们在输入数据集中（由 4524 个句子组成）找到了 300 个变量和 284 个因果对，均质化后减少到 70 个唯一变量和 119 个因果对。我们创建了一个描述这些关系的因果图，并对其进行分析，以展示可以获得的观点和与政策相关的见解。我们将这些与之前对政策工具进行的人工元审查进行比较，发现它们不仅大致一致，而且具有互补性。我们的结论是，尽管存在局限性，但这种方法可以帮助综合政策分析、政策制定和政策研究的因果证据。