A causal modeling perspective on decision theory
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从因果建模视角看决策理论
- 研究问题
- 能否用统一的因果建模语言明确决策理论中的主观与客观要素，并建立可比较不同决策规则表现的正式评价标准？
- 方法与思路
- 作者用非参数结构方程模型表示行动者和反事实，提出最大化主观反事实效用的个人决策理论，并以群体假想干预定义表现。
- 材料陈述的贡献
- 摘要称框架明确了证据决策理论与因果决策理论的定义，并在某些假设下证明个人决策理论对提出的表现指标最优。
- 阅读关注点
- 应核对主观模型与客观因果结构的关系、群体干预指标的含义、最优性所需假设，以及吸烟病变和纽科姆问题的形式化。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核验；摘要未列出最优性假设，结论也仅针对作者定义的评价指标。
原文摘要
Decision theory provides a formal framework for how agents should make choices under uncertainty, drawing on ideas from philosophy, probability, and causality. Despite significant progress, the field still lacks a unified modeling language, and key concepts - such as the distinction between subjective and objective elements, or what it means for a decision theory to perform well - are often left implicit. This can make it difficult to evaluate and compare competing theories, particularly in controversial cases. In this paper, we address these issues by introducing a formal framework for decision theory based on nonparametric structural equation models (NPSEMs), a well-established tool in causal inference. NPSEMs provide a unified foundation for representing agents, counterfactuals, and causal relationships, allowing for unambiguous definitions of EDT and CDT. Building on this foundation, we propose a novel decision theory - personal decision theory - which instructs agents to maximize a subjective model of their own counterfactual utility. We introduce a formal performance metric based on hypothetical interventions that enforce a given decision theory across a population - such as might be achieved through education or policy -- and show that, under certain assumptions, personal decision theory is optimal with respect to this metric. Throughout, we use the smoking lesion problem as a running example and conclude with a formal analysis of Newcomb's problem. Our aim is to provide decision theory with a clearer modeling language and firmer evaluative ground, thereby enabling more rigorous comparisons and facilitating conceptual progress in the field.