Comparison and contrast are the basic means to unveil causation and learn which treatments work. To build good comparison groups, randomized experimentation is key, yet often infeasible. In such non-experimental settings, we illustrate and discuss diagnostics to assess how well the common linear regression approach to causal inference approximates desirable features of randomized experiments, such as covariate balance, study representativeness, interpolated estimation, and unweighted analyses. We also discuss alternative regression modeling, weighting, and matching approaches and argue they should be given strong consideration in empirical work.

翻译：比较和对比是揭示因果关系并了解哪些干预措施有效的基本方法。要构建良好的比较组，随机实验是关键，但往往难以实施。在非实验情境下，我们阐述并讨论评估常用线性回归方法在因果推断中近似随机实验理想特性的诊断方法，如协变量平衡、研究代表性、插值估计及未加权分析。我们还讨论了替代的回归建模、加权和匹配方法，并认为实证研究中应给予这些方法充分考量。