Causal Inference with MNAR Self-Masking Confounders: A Stratified Delta-Imputed Propensity Estimation Method
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自遮蔽非随机缺失混杂变量下的因果推断：分层偏移插补倾向估计
- 研究问题
- 当混杂变量是否缺失取决于其自身未观测值时，如何结合插补与倾向得分加权估计平均处理效应？
- 方法与思路
- 作者提出 SDIPE，区分观测组与缺失组，对缺失混杂作偏移调整多重插补，再分别稳定加权并按样本占比合并。
- 材料陈述的贡献
- 摘要称，方法在所用模拟中偏差较低、覆盖率接近名义水平，且对偏移参数较稳健，并用于婚姻与抑郁分析。
- 阅读关注点
- 应核对偏移参数的含义与选取、分组效应合并的识别依据，以及插补不确定性是否完整进入标准误和区间。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收未核实；摘要未充分说明识别假设，其稳健性陈述不能推出 MNAR 下无假设识别。
原文摘要
In observational studies, causal inference becomes difficult when confounders are missing-not-at-random (MNAR), particularly where the missingness depends on the confounder's own unreported value (self-masking). Existing methods for handling MNAR confounders often rely on strong, unverifiable assumptions, leading to biased estimates. We propose a simple approach with Stratified Delta-Imputed Propensity Estimator (SDIPE) in the presence of self-masking confounders. SDIPE first stratifies data into observed and missing groups, imputes missing confounders via delta-adjusted multiple imputation. Then, within each group, average-treatment-effects (ATEs) are estimated by stabilized-inverse-probability-weights. The final ATE is obtained by combining the subgroup-specific estimates, weighted by respective proportions in the sample. Simulation study shows that SDIPE achieves low bias and near-nominal coverage (94-96%) across varying missingness, sample sizes, and treatment prevalence. In contrast, conventional sensitivity-based multiple imputation exhibits substantial bias and poor coverage (18-89%). Additionally, SDIPE is robust to the choice of the delta parameter. Applied to NHANES-2017-2018, SDIPE estimates that married individuals have a 1.19-point lower depression score than unmarried individuals (95% CI: -1.76, -0.64), adjusting for MNAR income data. SDIPE provides a practical and robust approach for causal inference with self-masking MNAR confounders, offering improved performance over existing methods without requiring restrictive assumptions about the missingness mechanism.