Comparison and contrast are the basic means to unveil causation and learn which treatments work. To build good comparisons that isolate the average effect of treatment from confounding factors, randomization is key, yet often infeasible. In such non-experimental settings, we illustrate and discuss how well the common linear regression approach to causal inference approximates features of randomized experiments, such as covariate balance, study representativeness, sample-grounded estimation, and unweighted analyses. We also discuss alternative regression modeling, weighting, and matching approaches. We argue they should be given strong consideration in empirical work.

翻译：比较与对比是揭示因果关系并了解何种干预措施有效的基本手段。要构建良好的比较，从而将处理效应的平均值与混杂因素分离，随机化是关键，但往往难以实现。在此类非实验性情境中，我们阐述并讨论常见的线性回归方法在因果推断中能如何近似随机化实验的特征，例如协变量平衡、研究代表性、基于样本的估计以及未加权分析。我们还探讨了替代性回归建模、加权和匹配方法，并主张在实证研究中应给予这些方法充分重视。