arXiv 2 Jun 2024 · Econometrics · publishedThe Journal of Economic Perspectives (2025) · 3 citations (OpenAlex)
arXiv:2406.00827 · PDF · DOI · OpenAlex · Extracted main text
In 1986, Robert LaLonde published an article comparing 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 their credibility. Following LaLonde's critical assessment, there have been significant methodological advances and practical changes, including (i) an emphasis on the unconfoundedness assumption separated from functional form considerations, (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) methods for estimating and exploiting treatment effect heterogeneity, and (v) a greater emphasis on validation exercises to bolster research credibility. To demonstrate the practical lessons from these advances, we reexamine the LaLonde 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 imply that these estimates are causally interpretable. 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rajeev H Dehejia and Sadek Wahba (1999) Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs | 1.000 | 11 | 4 | 100% |
| 2 | Paul R Rosenbaum and Donald B Rubin (1983) The central role of the propensity score in observational studies for causal effects | 1.000 | 5 | 3 | 100% |
| 3 | Robert J LaLonde (1986) Evaluating the econometric evaluations of training programs with experimental data | 0.973 | 25 | 6 | 92% |
| 4 | Susan Athey, Julie Tibshirani, Stefan Wager, et al (2019) Generalized random forests | 0.811 | 4 | 2 | 100% |
| 5 | Guido W Imbens and Donald B Rubin (2015) Causal Inference in Statistics, Social, and Biomedical Sciences self | 0.737 | 3 | 2 | 100% |
| 6 | Alberto Abadie and Guido W Imbens (2011) Bias-corrected matching estimators for average treatment effects self | 0.644 | 4 | 1 | 100% |
| 7 | Guido W Imbens (2015) Matching methods in practice: Three examples self | 0.644 | 4 | 1 | 100% |
| 8 | Alberto Abadie and Matias D Cattaneo (2018) Econometric methods for program evaluation | 0.644 | 2 | 2 | 100% |
| 9 | Sebastian Calónico and Jeffrey Smith (2017) The women of the national supported work demonstration | 0.644 | 2 | 2 | 100% |
| 10 | Richard K Crump, V Joseph Hotz, Guido W Imbens, and Oscar A Mitnik (2009) Dealing with limited overlap in estimation of average treatment effects self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 78 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Fragility in Average Treatment Effect on the Treated under Limited Covariate Support | 0.644 | 2 | 2 |
| 2 | TITLE | 0.511 | 2 | 1 |
| 3 | Factorial Difference-in-Differences | 0.405 | 1 | 1 |
| 4 | Post-Matching Two-Way Fixed Effects Estimation | 0.405 | 1 | 1 |