论文标题
关于线性模型中因果推断的零条件均值假设的作用
On the Role of the Zero Conditional Mean Assumption for Causal Inference in Linear Models
论文作者
论文摘要
许多计量经济学教科书暗示，在回归器的平均独立性和错误术语下，OLS参数具有因果解释。我们表明，即使满足了这一假设，OLS也可能确定没有因果解释的伪参数。即使假设线性模型是“结构”，也会在回归误差所代表的内容以及OLS估计是否是因果关系上产生歧义。此问题同样适用于线性IV和面板数据模型。为了给这些估计值进行因果解释，需要对“因果”模型（例如，使用潜在的结果框架）施加假设。这突出了因果推论需要因果关系，而不仅仅是随机的假设。
Many econometrics textbooks imply that under mean independence of the regressors and the error term, the OLS parameters have a causal interpretation. We show that even when this assumption is satisfied, OLS might identify a pseudo-parameter that does not have a causal interpretation. Even assuming that the linear model is "structural" creates some ambiguity in what the regression error represents and whether the OLS estimand is causal. This issue applies equally to linear IV and panel data models. To give these estimands a causal interpretation, one needs to impose assumptions on a "causal" model, e.g., using the potential outcome framework. This highlights that causal inference requires causal, and not just stochastic, assumptions.
