Bounding Causal Effects and Counterfactuals
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因果效应与反事实的界限估计
- 研究问题
- 当无未观测混杂或完全依从等假设难以成立时，如何比较并选择因果效应与反事实的界限算法以支持应用研究？
- 方法与思路
- 作者在统一评估框架中实现、扩展和比较符号、优化及信息论方法，并将熵约束方法扩展至必要且充分概率等反事实查询。
- 材料陈述的贡献
- 摘要称通过数千次模拟比较界限紧度、计算效率和假设违背稳健性，并提供算法决策树、选择模型及统一 Python 软件包。
- 阅读关注点
- 应核对各算法需要的假设和输入、界限是否锐利、连续情形如何计算，以及选择模型在新数据过程中的验证方式。
- 阅读边界
- 这是 arXiv 预印本，同行评审与接收未核实；摘要将作品称为学位论文，未提供算法细节或软件发布可用性的核验证据。
原文摘要
Causal inference often hinges on strong assumptions - such as no unmeasured confounding or perfect compliance - that are rarely satisfied in practice. Partial identification offers a principled alternative: instead of relying on unverifiable assumptions to estimate causal effects precisely, it derives bounds that reflect the uncertainty inherent in the data. Despite its theoretical appeal, partial identification remains underutilized in applied work, in part due to the fragmented nature of existing methods and the lack of practical guidance. This thesis addresses these challenges by systematically comparing a diverse set of bounding algorithms across multiple causal scenarios. We implement, extend, and unify state-of-the-art methods - including symbolic, optimization-based, and information-theoretic approaches - within a common evaluation framework. In particular, we propose an extension of a recently introduced entropy-bounded method, making it applicable to counterfactual queries such as the Probability of Necessity and Sufficiency (PNS). Our empirical study spans thousands of randomized simulations involving both discrete and continuous data-generating processes. We assess each method in terms of bound tightness, computational efficiency, and robustness to assumption violations. To support practitioners, we distill our findings into a practical decision tree for algorithm selection and train a machine learning model to predict the best-performing method based on observable data characteristics. All implementations are released as part of an open-source Python package, CausalBoundingEngine, which enables users to apply and compare bounding methods through a unified interface.