  In 1986, Robert LaLonde published an article that compared nonexperimental
estimates to experimental benchmarks (LaLonde 1986). He concluded that the
nonexperimental methods at the time could not systematically replicate
experimental benchmarks, casting doubt on the credibility of these methods.
Following LaLonde's critical assessment, there have been significant
methodological advances and practical changes, including (i) an emphasis on
estimators based on unconfoundedness, (ii) a focus on the importance of overlap
in covariate distributions, (iii) the introduction of propensity score-based
methods leading to doubly robust estimators, (iv) a greater emphasis on
validation exercises to bolster research credibility, and (v) methods for
estimating and exploiting treatment effect heterogeneity. To demonstrate the
practical lessons from these advances, we reexamine the LaLonde data and the
Imbens-Rubin-Sacerdote lottery data. We show that modern methods, when applied
in contexts with sufficient covariate overlap, yield robust estimates for the
adjusted differences between the treatment and control groups. However, this
does not mean that these estimates are valid. To assess their credibility,
validation exercises (such as placebo tests) are essential, whereas goodness of
fit tests alone are inadequate. Our findings highlight the importance of
closely examining the assignment process, carefully inspecting overlap, and
conducting validation exercises when analyzing causal effects with
nonexperimental data.
