论文标题
回归和因果关系
Regression and Causality
论文作者
论文摘要
干预措施（治疗/暴露）对结果的因果影响可以通过：i）指定有关数据生成过程的知识； ii）根据指定的知识（并给定测量的数据），可以确定目标数量的假设，例如因果比值比，例如因果关系量（例如因果关系）；然后，iii）使用适当的统计估计技术来估计感兴趣的所需参数。由于回归是统计分析的基石，因此显而易见：使用估计的回归参数进行因果效应估计是否合适？事实证明，使用回归进行效应估计是可能的，但通常需要比竞争方法更多的假设。本手稿提供了有关使用回归识别和估算因果参数所需的假设的全面摘要，同样重要，并且对统计实践的含义同样重要。
The causal effect of an intervention (treatment/exposure) on an outcome can be estimated by: i) specifying knowledge about the data-generating process; ii) assessing under what assumptions a target quantity, such as for example a causal odds ratio, can be identified given the specified knowledge (and given the measured data); and then, iii) using appropriate statistical estimation techniques to estimate the desired parameter of interest. As regression is the cornerstone of statistical analysis, it seems obvious to ask: is it appropriate to use estimated regression parameters for causal effect estimation? It turns out that using regression for effect estimation is possible, but typically requires more assumptions than competing methods. This manuscript provides a comprehensive summary of the assumptions needed to identify and estimate a causal parameter using regression and, equally important, discusses the resulting implications for statistical practice.
