Addressing Extreme Propensity Scores via the Overlap Weights

• Published in: American journal of epidemiology Vol. 188; no. 1; pp. 250 - 257
• Main Authors: Li, Fan, Thomas, Laine E
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.01.2019; Oxford Publishing Limited (England)
• Subjects: Bias; Causality; Confidence intervals; Epidemiologic Methods; Extreme values; Humans; Models, Statistical; Probabilistic inference; Propensity Score; Standard error; Statistical analysis; Variance; Weighting methods; epidemiologic methods; overlap weighting; trimming; clinical equipoise; inverse probability weighting; causal inference; statistical efficiency
• ISSN: 0002-9262; 1476-6256; 1476-6256
• DOI: 10.1093/aje/kwy201

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 (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.