Addressing Extreme Propensity Scores via the Overlap Weights

By Fan Li, Laine E. Thomas. American Journal of Epidemiology, 2018

708 citations recorded by the source corpus.

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 (i.e., remove them from the weighted analysis). However, such methods are often sensitive to the choice of cutoff points and discard a large proportion of the sample. The implications for bias and the precision of the treatment effect estimate are unclear. These problems are mitigated by a newly developed method, the 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. Here we use simulations to compare overlap weights to standard inverse probability weighting with trimming, in terms of bias, variance, and 95% confidence interval coverage. A range of propensity score distributions are considered, including settings with substantial nonoverlap and extreme values. To facilitate practical implementation, we further provide a consistent estimator for the standard error of the treatment effect estimated using overlap weighting.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6761d23773a2b9bd81d22176bbfca8adb5a9e1bf

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In Statistics and Probability

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Citations in the corpus

• An introduction to inverse probability of treatment weighting in observational research (2021, methodology): “Several weighting methods based on propensity scores are available, such as fine stratification weights [17], matching weights [18], overlap weights [19] and inverse probability of treatment weights—the focus of this article.”