学术活动

标题:

日期和时间: 

2025年1月10日（周五）
10:30-12:00

地点

主讲人:

When do linear regressions estimate causal effects in quasi-experiments? This paper provides a generic diagnostic that assesses whether a given linear regression specification on a given dataset admits a design-based interpretation. To do so, we define a notion of potential weights, which encode counterfactual decisions a given regression makes to unobserved potential outcomes. If the specification does admit such an interpretation, this diagnostic can find a vector of unit-level treatment assignment probabilities---which we call an implicit design---under which the regression estimates a causal effect. This diagnostic also finds the implicit causal effect estimand. Knowing the implicit design and estimand adds transparency, leads to further sanity checks, and opens the door to design-based statistical inference. When applied to regression specifications studied in the causal inference literature, our framework recovers and extends existing theoretical results. When applied to widely-used specifications not covered by existing causal inference literature, our framework generates new theoretical insights.

主讲人简介:

陈稼丰老师目前于斯坦福经济政策研究所（SIEPR）担任博士后研究员，他将于2025年7月开始担任斯坦福大学经济学助理教授。在此之前，他在哈佛大学获得了博士学位、硕士学位和学士学位。他的研究兴趣包括理论计量经济学和因果推断。