Data Fusion for Partial Identification of Causal Effects
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用于因果效应部分识别的数据融合
- 研究问题
- 当无未观测混杂和跨数据来源反事实可交换性同时失效时，如何融合试验与观测数据，判断因果效应方向及结论稳健性？
- 方法与思路
- 作者用可解释的敏感性参数描述两类假设违背，推导因果效应界，构造双重稳健估计量，并开展结论失效边界分析。
- 材料陈述的贡献
- 摘要称该框架可评估同时违背关键假设时的效应界；Project STAR 应用显示总体及若干子组结论具有稳健性。
- 阅读关注点
- 核对敏感性参数的含义与可接受范围、双重稳健性针对哪些辅助模型，以及失效边界和子组分析的不确定性处理。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未给出效应界数值及稳健范围，不能据此排除未测量偏差。
原文摘要
Data fusion techniques integrate information from heterogeneous data sources to improve learning, generalization, and decision making across data sciences. In causal inference, these methods leverage rich observational data to improve causal effect estimation, while maintaining the trustworthiness of randomized controlled trials. Existing approaches often relax the strong no unobserved confounding assumption by instead assuming exchangeability of counterfactual outcomes across data sources. However, when both assumptions simultaneously fail - a common scenario in practice - current methods cannot identify or estimate causal effects. We address this limitation by proposing a novel partial identification framework that enables researchers to answer key questions such as: Is the causal effect positive or negative? and How severe must assumption violations be to overturn this conclusion? Our approach introduces interpretable sensitivity parameters that quantify assumption violations and derives corresponding causal effect bounds. We develop doubly robust estimators for these bounds and operationalize breakdown frontier analysis to understand how causal conclusions change as assumption violations increase. We apply our framework to the Project STAR study, which investigates the effect of classroom size on students' third-grade standardized test performance. Our analysis reveals that the Project STAR results are robust to simultaneous violations of key assumptions, both on average and across various subgroups of interest. This strengthens confidence in the study's conclusions despite potential unmeasured biases in the data.