Counterfactuals and Causal Inference: Methods and Principles for Social Research (Paperback)

In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.

在這本《反事實和因果推論》的第二版中，我們對觀察數據分析中的反事實方法的基本特點進行了全面修訂和擴展，並通過社會、人口和健康科學的例子進行了演示。首先介紹了使用潛在結果模型和因果圖的替代估計技術；之後，從潛在結果的角度介紹了匹配和回歸等調節技術。然後，我們介紹了在重要的因果暴露因素未被觀察到的研究情境中的替代技術，例如儀器變量估計器、長期方法和通過因果機制進行估計。全書強調了因果效應的異質性的重要性，並討論了通過機制進行深入因果解釋的需求。