Using Experiments to Correct for Selection in Observational Studies
利用实验纠正观察性研究中的选择偏差AI 解读 ↓
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利用实验纠正观察性研究中的选择偏差
- 研究问题
- 当观察数据同时包含主要与次要结果，而随机实验只观察次要结果时，如何利用实验信息纠正选择偏差并估计主要结果的处理效应？
- 方法与思路
- 作者利用次要结果与基于实验处理效应的预测值之差衡量选择偏差，再控制该偏差；识别依赖作者提出的潜在无混杂假设。
- 材料陈述的贡献
- 摘要称该假设弱于常用替代结果估计的假设，并报告班级规模减少25%对应高中毕业率提高0.7个百分点的估计结果。
- 阅读关注点
- 应核对潜在无混杂的完整定义、实验信息如何迁移至观察样本，以及偏差代理变量的构造和估计误差如何进入最终推断。
- 阅读边界
- 依据 arXiv 预印本摘要，不能确认同行评审或期刊接收；所报教育效应是作者估计，摘要不足以检验识别假设与跨样本可比性。
原文摘要
Researchers increasingly have access to two types of data: (i) large observational datasets where treatment (e.g., class size) is not randomized but several primary outcomes (e.g., graduation rates) and secondary outcomes (e.g., test scores) are observed and (ii) experimental data in which treatment is randomized but only secondary outcomes are observed. We develop a new method to estimate treatment effects on primary outcomes in such settings. We use the difference between the secondary outcome and its predicted value based on the experimental treatment effect to measure selection bias in the observational data. Controlling for this estimate of selection bias yields an unbiased estimate of the treatment effect on the primary outcome under a new assumption that we term latent unconfoundedness, which requires that the same confounders affect the primary and secondary outcomes. Latent unconfoundedness weakens the assumptions underlying commonly used surrogate estimators. We apply our estimator to identify the effect of third grade class size on students outcomes. Estimated impacts on test scores using OLS regressions in observational school district data have the opposite sign of estimates from the Tennessee STAR experiment. In contrast, selection-corrected estimates in the observational data replicate the experimental estimates. Our estimator reveals that reducing class sizes by 25% increases high school graduation rates by 0.7 percentage points. Controlling for observables does not change the OLS estimates, demonstrating that experimental selection correction can remove biases that cannot be addressed with standard controls.