共形化李推断：单调样本选择下无分布个体治疗效果区间
Conformalized Lee Inference: Distribution-Free Prediction Sets for Individual Treatment Effect under Monotone Sample Selection
- Graduate School of Economics, University of Tokyo（东京大学大学院经济学研究科）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
研究单侧选择随机研究中的预测问题，提出共形化李程序，利用处理后的选定观测值训练和检验预测规则，通过观测到的处理-对照选择差距调整临界值，提供可靠覆盖率。
中文摘要 AI 辅助
实证研究常仅观察选定单位的结果，且处理可能改变观察对象。本文研究单侧选择随机研究中的预测。标准预测区间可能失败，提出的共形化李程序用处理后的选定观测值训练和检验预测规则，调整临界值，为选定对照生成个体治疗效果区间，无需正确指定预测规则即可提供可靠覆盖率。
英文摘要
Treatment can change which outcomes are observed, so treated-selected and selected-control units need not represent the same latent population. This paper proposes conformalized Lee inference for counterfactual prediction under randomized treatment and monotone sample selection. The procedure uses treated-selected observations to fit and calibrate an arbitrary prediction rule and replaces the usual $(1-α)$ score quantile with the adjusted $(1-απ)$ quantile, where $π$ is the identified share of always-selected units among treated-selected units. The resulting prediction set has finite-sample, distribution-free marginal coverage over the sharp Lee ambiguity set. For a selected-control unit, subtracting the observed untreated outcome yields a marginal prediction set for the realized individual treatment effect. The adjusted population cutoff is minimax optimal over the reduced-information identification region. Simulations show that ordinary split-conformal prediction can under-cover under distribution shifts induced by selection, whereas the adjusted procedures restore coverage. Empirical analysis uses the National Job Corps Study data to illustrate prediction sets for individual wage effects of assignment to program access.
