Optimal Causal Annotations: An Application to Casenotes in Social Services
最优因果标注：社会服务个案记录的应用AI 解读 ↓
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最优因果标注：社会服务个案记录的应用
- 研究问题
- 当真实结局需要昂贵专家标注，而大语言模型标注可能有偏时，固定预算下应选择哪些观测，以提高平均处理效应估计精度？
- 方法与思路
- 作者通过优化标注概率最小化平均处理效应估计方差，推导闭式方案，并构造可行的两批次估计器用于文本结局标注。
- 材料陈述的贡献
- 摘要称，两批次估计器达到最优渐近方差，经验比较减少同等区间宽度所需标注成本；服务案例也显示直接使用模型会低估进展。
- 阅读关注点
- 应核对最优方差对应的设计约束、标注抽样与因果识别条件，以及第一批次如何支持后续分配和专家结局的可靠性。
- 阅读边界
- 这是预印本，同行评审及期刊接收未核实。成本节省和服务效应来自所述数据设定，摘要未说明其向其他预算与人群外推的条件。
原文摘要
Problem definition: Estimating causal effects of interventions is central to policy and operations, but outcome data are often missing or costly to obtain. LLMs can provide text annotation at scale but may be subject to unknown bias. When ground-truth outcomes require expensive expert labeling or follow-up, budget limits typically allow only a fraction of the data to be labeled. Motivated by collaboration with a nonprofit conducting street outreach in homelessness services, whose most interesting outcomes are in unstructured casenotes, we ask: which observations should be selected for labeling under a fixed budget? Methodology/results: Our method optimizes annotation probabilities to minimize the variance of average treatment effect estimation. We derive a closed-form solution and establish that a feasible two-batch estimator achieves the best possible asymptotic variance. On simulated and real-world datasets, our method achieves lower MSE than random sampling and 43%-91% reductions in labels and costs for the same interval widths. In a case study classifying progress towards housing applications from casenotes with LLMs, we find out-of-the-box LLMs under-recognize client progress, highlighting the importance of grounding LLM judgment. Our estimates indicate that 8.6% of clients improve 2-year housing outcomes due to more street outreach in the first six months, and increasing outreach from the first to third quartile increases maximum progress towards a housing application by an estimated half-step. Managerial implications: LLM-as-a-judge may threaten estimation validity; our method enables valid causal estimation with a limited annotation budget. Evaluating causal impacts on intermediate nonprofit outcomes can inform operations: returns to outreach appear concave, so expanding the extensive margin of who receives outreach may be resource-efficient.