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We consider two recent suggestions for how to perform an empiricallymotivated Monte Carlo study to help select a treatment effect estimator underunconfoundedness. We show theoretically that neither is likely to beinformative except under restrictive conditions that are unlikely to besatisfied in many contexts. To test empirical relevance, we also apply theapproaches to a real-world setting where estimator performance is known. Bothapproaches are worse than random at selecting estimators which minimiseabsolute bias. They are better when selecting estimators that minimise meansquared error. However, using a simple bootstrap is at least as good and oftenbetter. For now researchers would be best advised to use a range of estimatorsand compare estimates for robustness.

Mostly Harmless Simulations? Using Monte Carlo Studies for Estimator Selection