Regularized calibrated estimation of propensity scores with model misspecification and high-dimensional data

Understand

Propensity score methods are widely used for estimating treatment effects from observational studies.

• A popular approach is to estimate propensity scores by maximum likelihood based on logistic regression, and then apply inverse probability weighted estimators or extensions to estimate treatment effects.
• However, a challenging issue is that such inverse probability weighting methods including doubly robust methods can perform poorly even when the logistic model appears adequate as examined by conventional techniques.
• In addition, there is increasing difficulty to appropriately estimate propensity scores when dealing with a large number of covariates.