Peering Inside the Black Box: Exploring Innovations in Interpretable Machine Learning and Causal Inference for the Explanation of Political Violence

窥视黑匣子：探索可解释的机器学习和因果推理的创新以解释政治暴力

• 批准号: 2901789
• 金额: --
• 依托单位: London School of Economics and Political Science
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2901789
• 关键词: Peering; Inside; Black; Box; Exploring

The use of machine learning (ML) within social science research is increasingly common. Yet, a gap remains in how we use these models in inference settings to explain, rather than predict or classify, phenomena. Concurrently, there is a growing need in policymaking to make sense of increasingly common, but complex, machine learning strategies. Despite ML models being able to capture the complexity of social phenomena to a greater extent than traditional statistical models, they have been considered less useful for explanations and inferences, in part due to being viewed as 'black box' methods that are substantively opaque. Recent methodological advances show that we can make sense of these complex ML models and use them for both explanation and causal inferences. These advances are grouped under the subfields of 'Interpretable machine learning' (IML) and 'Causal machine learning' (Causal ML). To date, these innovations have not been sufficiently translated within the social sciences. This research aims to fill this gap and demonstrate the utility of using ML methods not just for prediction, but for explanation and inference also. I will do so by engaging in translation, innovation, and finally application of these methods to the study of political violence. Studying the application of ML methods in political violence is particularly important as understanding the determinants of conflict allow proactive interventions and policy solutions in various forms. Political violence is, however, complex, and multi-causal, and therefore conventional parametric methods are unlikely to model these dynamics effectively. For policymaking relevance, it is imperative that we can explain why a conflict event might occur as well as if and when. Methodologically, I aim to demonstrate how researchers can use ML to capture and explain complex social phenomena, and in the process open avenues for greater policymaking relevance and impact. A broad research question that could apply to this PhD project, then, is "How can we leverage developments in complex machine learning strategies for explanation and inference in social science contexts?" Substantively, I aim to use these tools to advance our understanding of the mechanisms of political violence.

在社会科学研究中使用机器学习（ML）越来越普遍。然而，在推理环境中使用这些模型来解释而不是预测或分类现象的差距仍然存在。同时，政策制定的需求日益增长，以使越来越普遍但复杂的机器学习策略有意义。尽管ML模型能够比传统统计模型更大程度地捕获社会现象的复杂性，但它们被认为对解释和推论的有用程度较小，部分原因是被视为实质上不透明的“黑匣子”方法。最近的方法学进步表明，我们可以理解这些复杂的ML模型，并将其用于解释和因果推断。这些进步分为“可解释的机器学习”（IML）和“因果机学习”（因果ML）的子场。迄今为止，这些创新尚未在社会科学中得到足够的翻译。这项研究旨在填补这一空白，并证明不仅用于预测，而且还用于解释和推论的实用性。我将通过从事翻译，创新，最后将这些方法应用于政治暴力研究中。研究ML方法在政治暴力中的应用尤为重要，因为了解冲突的决定因素可以主动采取各种形式的干预措施和政策解决方案。然而，政治暴力是复杂的，而且是多因果的，因此传统的参数方法不太可能有效地建模这些动态。对于政策的相关性，我们必须解释为什么可能发生冲突事件以及何时以及何时发生冲突事件。从方法上讲，我旨在证明研究人员如何使用ML来捕获和解释复杂的社会现象，并在此过程中开放了更大的政策相关性和影响力的途径。那么，一个可以适用于该博士学位项目的广泛研究问题是“我们如何利用复杂的机器学习策略中的发展来解释和推断社会科学环境中？”实质上，我旨在使用这些工具来促进我们对政治暴力机制的理解。