Arbeitspapier

Mostly harmless simulations? On the internal validity of empirical Monte Carlo studies

Currently there is little practical advice on which treatment effect estimator to use when trying to adjust for observable differences. A recent suggestion is to compare the performance of estimators in simulations that somehow mimic the empirical context. Two ways to run such "empirical Monte Carlo studies" (EMCS) have been proposed. 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. We find that in our setting both EMCS 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.

Language
Englisch

Bibliographic citation
Series: cemmap working paper ; No. CWP56/18

Classification
Wirtschaft
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
Model Evaluation, Validation, and Selection
Subject
empirical Monte Carlo studies
program evaluation
selection on observables
treatment effects

Event
Geistige Schöpfung
(who)
Advani, Arun
Kitagawa, Toru
S±oczy´nski, Tymon
Event
Veröffentlichung
(who)
Centre for Microdata Methods and Practice (cemmap)
(where)
London
(when)
2018

DOI
doi:10.1920/wp.cem.2018.5618
Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Advani, Arun
  • Kitagawa, Toru
  • S±oczy´nski, Tymon
  • Centre for Microdata Methods and Practice (cemmap)

Time of origin

  • 2018

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