AI 赋能科研 · 高效学习 · 智慧成长
DID · 工具变量 · RDD · 面板数据 —— 原理 × 直觉 × 代码 × AI 顶刊方法每周追踪 · Stata 代码速查 · 科研 AI 提示词库
搜索知识库、代码、Prompt…⌘K最新文章
因果推断方法最新讲解
What Is Multicollinearity? How to Detect, Test, and Address It?
Suppose you run a wage regression with years of education as the key variable. All coefficients look normal. Then a reviewer suggests you 'add another control variable'—you do so, adding 'mother's years of education.'
What is the relationship between endogenous and exogenous explanatory variables? When should you worry about endogeneity?
$$ \\text{Wage} = \\beta0 + \\beta1 \\cdot \\text{Education} + \\beta2 \\cdot \\text{Experience} + \\varepsilon $$
How Should We Understand \"Ceteris Paribus\" in Multiple Regression?
Open any empirical paper that uses multiple regression, and you will most likely see this sentence in the section interpreting the coefficients:
Why Does OLS Produce Just One Number, Yet We Call It \"Unbiased\" and \"Efficient\"?
Anyone who has studied introductory econometrics has experienced this cognitive moment—
Everyone Cares About Causality, So What Is the Point of "Correlational" Data Analysis?
If you follow methodological debates in the social sciences in recent years, you will surely feel a powerful trend—causal inference has come to dominate the discourse almost entirely.
How to Interpret Coefficient Confidence Intervals in Stata Regression Output? What Is Their Relationship with p-values?
wage | Coefficient Std. err. t P|t| [95% conf. interval] + education | .0800000 .0100000 8.00 0.000 .060399 .0996001 ...
代码库
Stata .do 文件速查
AI Prompt 精选
科研写作提示词
撰写实证论文引言部分，需要清晰说明研究问题、贡献和识别策略
需要对原始数据进行清洗和预处理
依据 GB/T 7714-2015 对参考文献题录进行规范和修正
顶刊方法追踪
2026 Q1 最新报告
进入 2026 年，顶刊实证在「大规模行政数据 + 清晰识别」的范式上延续，同时统计与机器学习期刊（JASA、JBES）与经济理论顶刊（Econometrica）在因果推断上的对话更密集：一方面，交错 DID、事件研究与非参数偏差讨论仍是发表与复现的焦点；另一方面，面向高维协变量与复杂响应的 DML、随机实验中异质性效应的后处理推断、以及针对未测混杂的敏感度分析框架，正在成为连接 AI 科研工具与可发表计量标准的关键接口。
扫码关注
我们每 1-2 周整理一次精选推送，包括顶刊方法追踪、Stata 代码实操与 AI 科研资源。