# Propensity score weighting under limited overlap and model misspecification

@article{Zhou2020PropensitySW, title={Propensity score weighting under limited overlap and model misspecification}, author={Yunji Zhou and Roland A. Matsouaka and Laine E Thomas}, journal={Statistical Methods in Medical Research}, year={2020}, volume={29}, pages={3721 - 3756} }

Propensity score weighting methods are often used in non-randomized studies to adjust for confounding and assess treatment effects. The most popular among them, the inverse probability weighting, assigns weights that are proportional to the inverse of the conditional probability of a specific treatment assignment, given observed covariates. A key requirement for inverse probability weighting estimation is the positivity assumption, i.e. the propensity score must be bounded away from 0 and 1. In…

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## References

SHOWING 1-10 OF 65 REFERENCES

### Addressing Extreme Propensity Scores via the Overlap Weights

- MathematicsAmerican journal of epidemiology
- 2019

Simulations are used to compare overlap weights to standard inverse probability weighting with trimming, in terms of bias, variance, and 95% confidence interval coverage, and provide a consistent estimator for the standard error of the treatment effect estimated using overlap weighting.

### Propensity score weighting analysis and treatment effect discovery

- Mathematics, EconomicsStatistical methods in medical research
- 2018

This work develops 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.

### Propensity Score Weighting for Causal Inference with Multi-valued Treatments

- Mathematics
- 2018

This article proposes a unified framework, the balancing weights, for estimating causal effects with multi-valued treatments using propensity score weighting. These weights incorporate the…

### Balancing Covariates via Propensity Score Weighting

- Mathematics
- 2018

ABSTRACT Covariate balance is crucial for unconfounded descriptive or causal comparisons. However, lack of balance is common in observational studies. This article considers weighting strategies for…

### Constructing inverse probability weights for marginal structural models.

- EconomicsAmerican journal of epidemiology
- 2008

The authors describe possible tradeoffs that an epidemiologist may encounter when attempting to make inferences and weight truncation is presented as an informal and easily implemented method to deal with these tradeoffs.

### The central role of the propensity score in observational studies for causal effects

- Economics
- 1983

Abstract : The results of observational studies are often disputed because of nonrandom treatment assignment. For example, patients at greater risk may be overrepresented in some treatment group.…

### Weight Trimming and Propensity Score Weighting

- PsychologyPloS one
- 2011

Although trimming can improve inferences in some settings, in order to consistently improve the performance of propensity score weighting, analysts should focus on the procedures leading to the generation of weights rather than relying on ad-hoc methods such as weight trimmed.

### An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

- EconomicsMultivariate behavioral research
- 2011

The propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects, and different causal average treatment effects and their relationship with propensity score analyses are described.

### Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score

- Economics, Mathematics
- 2000

It is shown that weighting with the inverse of a nonparametric estimate of the propensity Score, rather than the true propensity score, leads to efficient estimates of the various average treatment effects, whether the pre-treatment variables have discrete or continuous distributions.

### Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data

- Mathematics
- 2006

When outcomes are missing for reasons beyond an investigator's control, there are two different ways to adjust a parameter estimate for covariates that may be related both to the outcome and to…