Addressing Extreme Propensity Scores via the Overlap Weights.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Li, F; Thomas, LE

Published Date

  • January 1, 2019

Published In

Volume / Issue

  • 188 / 1

Start / End Page

  • 250 - 257

PubMed ID

  • 30189042

Pubmed Central ID

  • 30189042

Electronic International Standard Serial Number (EISSN)

  • 1476-6256

Digital Object Identifier (DOI)

  • 10.1093/aje/kwy201

Language

  • eng

Conference Location

  • United States