Addressing Extreme Propensity Scores via the Overlap Weights

Abstract

The popular inverse probability weighting method in causal inference is often hampered by extreme propensity scores, resulting in biased estimates and excessive variance. A common remedy is to trim patients with extreme scores. However, such methods are often sensitive to the choice of cutoff points and discard a large proportion of the sample. The implications to bias and precision of the treatment effect estimate are unclear. These problems are mitigated by the newly developed overlap weighting method. Overlap weights emphasize the target population with the most overlap in observed characteristics between treatments, by continuously down-weighting the units in the tails of the propensity score distribution. We use simulations to compare overlap weights to standard inverse probability weighting with trimming, in terms of bias, variance and confidence interval coverage. A range of propensity score distributions are considered, including settings with substantial non-overlap and extreme values. To facilitate practical implementation, we further provide a consistent estimator for standard error of the treatment effect estimated using overlap weighting.