基于双重差分法学习说服的效应
Learning the Effect of Persuasion via Difference-In-Differences
AI总结：
该研究提出双重差分框架，引入向前、向后平均说服率，结合GMM等方法，验证了其在分阶段课程改革中的实用性。
摘要：
我们提出了一种双重差分框架，用于衡量在交错处理情境下信息处理对行为的说服影响。我们引入了两个因果参数：已处理组的向前平均说服率和向后平均说服率，二者是对已处理组平均处理效应的细化。向前率排除了“对已信服者说教”的情况，向后率则忽略了“对牛弹琴”的情况，且向后率与因果概率文献中的必要性概率一致。我们在无反冲条件和响应概率已知变换的平行趋势假设下识别这两个说服率，以恒等链接为基准，非线性链接作为敏感性检验。我们利用广义矩估计（GMM）和有限信息方法开展估计与推断，并将该框架应用于不同省份分阶段推行的中国课程改革，验证了其实用性。
英文摘要：
We develop a difference-in-differences framework to measure the persuasive impact of informational treatments on behavior in staggered treatment settings. We introduce two causal parameters, the forward and backward average persuasion rates on the treated, which refine the average treatment effect on the treated. The forward rate excludes cases of "preaching to the converted," while the backward rate omits "talking to a brick wall" cases. The backward rate coincides with the probability of necessity from the literature on probabilities of causation. We identify both persuasion rates under a no-backlash condition and a parallel-trends assumption imposed on a known transformation of response probabilities, taking the identity link as the baseline and nonlinear links as sensitivity checks. We develop estimation and inference using GMM and a limited-information method. We demonstrate the usefulness of our framework with an application to a Chinese curriculum reform introduced across provinces at different times.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
