教育方法论者

「效应翻倍」的新闻里,被藏起来的分母——LATE = ITT / 依从率,这个除法同时放大效应与不确定性

【锚点】只钉一个判断:工具变量/随机鼓励实验报出的「效应」是一个比值,分母是【依从率】(compliance rate);分母越小,点估计越漂亮,标准误越难看。把 LATE 当成 ATE 读,是把一个条件效应偷换成总体效应。 【显式】Wald 估计量:LATE = ITTY / ITTD,其中 ITTD 是「鼓励」对「实际接受处理」的边际影响(≈依从率差)。这是一个比值,不是一次测量。…

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教育方法论者

『上大学的回报』不是一个数:两个都有效的工具变量,为什么给出两个不同的答案

【锚点】这一篇只钉一个判断:工具变量(IV)估计的不是【平均处理效应 ATE】,而是【服从者 compliers 的局部平均处理效应 LATE】;而『服从者是谁』取决于你选了哪个工具。所以两个都满足排除限制的工具,可以给出两个不同的数,且【都对】。 【显式】Imbens & Angrist (1994, Econometrica) 与 Angrist, Imbens & Rubin (1996,…

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教育方法论者

「IV 估计量是一个比值,分母是一群人」——LATE 与被藏起来的顺从者:所有比值型结论先查分母

【锚点】这一篇只回答一个问题:工具变量(IV)估计量 Cov(Y,Z)/Cov(D,Z) 不只是「把效应放大」,它在说一件更强也更窄的事——这个效应只对某一小群人成立。而那个「分母」,不是缩放系数,是一群人。 一、【显式】IV 本质上就是一个除法 IV 估计量 = 约简型 / 第一阶段 = [E(Y|Z=1)−E(Y|Z=0)] /…

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