Some Common Pitfalls in Causal Analysis of Categorical Data
讨论了分类数据分析中因果解释的常见陷阱，如遗漏变量、遗漏交互、对比编码不当和交互结构误设，并展示了对数线性模型和卡方自动交互检测如何帮助研究者获得因果洞见。
Examples of some common pitfalls in the analysis of categorical data are discussed in the context of causal interpretation of the results. Though no statistical technique can replace theory, the author shows that log-linear modeling and chi square automatic interaction detection can provide researchers with powerful tools for gaining valuable causal insights into their data. Examples include the biasing effects of omitted variables, omitted interactions, improper contrast coding, and misspecification of the structure of an hypothesized interaction.