标题： 因果建模简介 显示英文标题

标题： An introduction to Causal Modelling

摘要： 本教程通过整合潜在结果和图形方法，提供现代因果建模的简明介绍。 我们探讨因果问题，如干预下的反事实推理，并定义二元处理和潜在结果。 我们讨论因果效应度量——包括对接受处理组和未接受处理组的平均处理效应——以及二元结果的效应尺度选择。 我们在可交换性和一致性下推导随机实验中的识别，并扩展到分层和阻断设计。 我们介绍倾向得分估计的逆概率加权和通过夹心估计量进行的稳健推断。 最后，我们介绍因果图、d-分离、后门准则、单世界干预图和结构方程模型，展示图形方法和潜在结果方法如何相互补充。 重点放在清晰的符号、直观的解释和应用研究者的实际例子上。 显示英文摘要

摘要： This tutorial provides a concise introduction to modern causal modeling by integrating potential outcomes and graphical methods. We motivate causal questions such as counterfactual reasoning under interventions and define binary treatments and potential outcomes. We discuss causal effect measures-including average treatment effects on the treated and on the untreated-and choices of effect scales for binary outcomes. We derive identification in randomized experiments under exchangeability and consistency, and extend to stratification and blocking designs. We present inverse probability weighting with propensity score estimation and robust inference via sandwich estimators. Finally, we introduce causal graphs, d-separation, the backdoor criterion, single-world intervention graphs, and structural equation models, showing how graphical and potential-outcome approaches complement each other. Emphasis is placed on clear notation, intuitive explanations, and practical examples for applied researchers.