Bayesian probability

Accounting

Radiology

Business

Propensity score matching

Inverse probability weighting

Mathematics

Posterior probability

Average treatment effect

Gene

Statistics

Econometrics

Estimator

Weighting

Chemistry

Variance (accounting)

Computer science

Statistical power

Inverse probability

Biochemistry

Robustness (evolution)

Medicine

Statistical Methods in Medical Research

Inverse probability weighting can be used to estimate the average treatment effect in propensity score analysis. When there is lack of overlap in the propensity score distributions between the treatment groups under comparison, some weights may be excessively large, causing numerical instability and bias in point and variance estimation. We study a class of modified inverse probability weighting estimators that can be used to avoid this problem. These weights cause the estimand to deviate from the average treatment effect. We provide some justification for this deviation from the perspective of treatment effect discovery. We show that when lack of overlap occurs, the modified weights can achieve substantial gains in statistical power compared with inverse probability weighting and other propensity score methods. We develop analytical variance estimates that properly adjust for the sampling variability of the estimated propensity scores, and augment the modified inverse probability weighting estimator with outcome models for improved efficiency, a property that resembles double robustness. Results from extensive simulations and a real data application support our conclusions. The proposed methodology is implemented in R package PSW.

Propensity score weighting analysis and treatment effect discovery