Comparing Experimental and Nonexperimental Methods: What Lessons Have We Learned Four Decades After LaLonde (1986)?
比较实验与非实验方法：LaLonde（1986）四十年后的经验教训AI 解读 ↓
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比较实验与非实验方法：LaLonde（1986）四十年后的经验教训
- 研究问题
- 现代非实验方法在何种分析环境下能稳定估计处理组与对照组的调整后差异，又需要哪些验证才能支持因果解释？
- 方法与思路
- 作者回顾无混杂、协变量重叠、倾向得分、双重稳健及异质性方法的发展，并重新分析 LaLonde 数据以展示实践教训。
- 材料陈述的贡献
- 摘要称，充分重叠时现代方法可得到稳健的调整后差异，但因果可信度仍需安慰剂等验证，拟合优度检验不足以替代。
- 阅读关注点
- 应核对处理分配机制、重叠诊断和样本筛选如何影响比较，以及验证练习究竟检验哪些威胁、仍遗漏哪些偏误。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出具体估计结果，稳健的组间差异本身不能证明因果解释。
原文摘要
In 1986, Robert LaLonde published an article comparing nonexperimental estimates to experimental benchmarks (LaLonde 1986). He concluded that the nonexperimental methods at the time could not systematically replicate experimental benchmarks, casting doubt on their credibility. Following LaLonde's critical assessment, there have been significant methodological advances and practical changes, including (i) an emphasis on the unconfoundedness assumption separated from functional form considerations, (ii) a focus on the importance of overlap in covariate distributions, (iii) the introduction of propensity score-based methods leading to doubly robust estimators, (iv) methods for estimating and exploiting treatment effect heterogeneity, and (v) a greater emphasis on validation exercises to bolster research credibility. To demonstrate the practical lessons from these advances, we reexamine the LaLonde data. We show that modern methods, when applied in contexts with sufficient covariate overlap, yield robust estimates for the adjusted differences between the treatment and control groups. However, this does not imply that these estimates are causally interpretable. To assess their credibility, validation exercises (such as placebo tests) are essential, whereas goodness-of-fit tests alone are inadequate. Our findings highlight the importance of closely examining the assignment process, carefully inspecting overlap, and conducting validation exercises when analyzing causal effects with nonexperimental data.