Arun Advani, Toru Kitagawa, Tymon Słoczyński
arXiv 25 Sep 2018 · Econometrics · publishedJournal of Applied Econometrics (2019) · 16 citations (OpenAlex)
arXiv:1809.09527 · PDF · DOI · OpenAlex · Extracted main text
We consider two recent suggestions for how to perform an empirically motivated Monte Carlo study to help select a treatment effect estimator under unconfoundedness. We show theoretically that neither is likely to be informative except under restrictive conditions that are unlikely to be satisfied in many contexts. To test empirical relevance, we also apply the approaches to a real-world setting where estimator performance is known. Both approaches are worse than random at selecting estimators which minimise absolute bias. They are better when selecting estimators that minimise mean squared error. However, using a simple bootstrap is at least as good and often better. For now researchers would be best advised to use a range of estimators and compare estimates for robustness.
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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 | Smith, J. A. and P. E. Todd (2005) Does Matching Overcome LaLonde's Critique of Nonexperimental Estimators? | 1.000 | 12 | 3 | 100% |
| 2 | Dehejia, R. H. and S. Wahba (1999) Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs | 1.000 | 8 | 3 | 100% |
| 3 | Huber, M., M. Lechner, and C. Wunsch (2013) The Performance of Estimators Based on the Propensity Score | 0.971 | 12 | 5 | 92% |
| 4 | Busso, M., J. DiNardo, and J. McCrary (2014) New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators | 0.961 | 9 | 4 | 89% |
| 5 | LaLonde, R. J (1986) Evaluating the Econometric Evaluations of Training Programs with Experimental Data | 0.874 | 9 | 2 | 100% |
| 6 | Advani, A., T. Kitagawa, and T. Soczyński (2019) Supplementary Appendix for `Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection' self | 0.874 | 6 | 2 | 100% |
| 7 | Abadie, A. and G. W. Imbens (2011) Bias-Corrected Matching Estimators for Average Treatment Effects | 0.843 | 4 | 3 | 75% |
| 8 | Dehejia, R. H. and S. Wahba (2002) Propensity Score-Matching Methods for Nonexperimental Causal Studies | 0.737 | 3 | 2 | 100% |
| 9 | Heckman, J. J. and V. J. Hotz (1989) Choosing Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training | 0.737 | 3 | 2 | 100% |
| 10 | Smith, J. A. and P. E. Todd (2001) Reconciling Conflicting Evidence on the Performance of Propensity-Score Matching Methods | 0.737 | 3 | 2 | 100% |
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