LAUSR

In observational studies, propensity score weighting methods are regarded as the conventional standard for estimating the effects of treatments on outcomes. We introduce entropy balancing, which despite its excellent conceptual properties, has been under-utilized in the applied studies. Using an extensive series of Monte Carlo simulations, we evaluated the performance of entropy balancing, in estimating difference in means, marginal odds ratios, rate ratios and hazard ratios. The performance of entropy balancing was relatively compared with that of inverse probability of treatment weighting using the propensity score. We found that entropy balancing outperformed the IPW method in estimating difference in means, marginal odds ratios, and hazard ratios, but when estimating marginal rate ratios, IPW performed better. Entropy balancing produced more biased estimates in many cases. However, the entropy balancing algorithm is capable of controlling bias by loosening the tightening of the pre-specified tolerance on covariate balance. We report findings as to when one technique is better than the other with no proclamation on whether one method is in every case superior to the other. Entropy balancing merits more widespread adoption in applied studies.