  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.
