Some Methods of Propensity‐Score Matching had Superior Performance to Others: Results of an Empirical Investigation and Monte Carlo simulations

By Peter C. Austin. Biometrical journal. Biometrische Zeitschrift, 2009

778 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/167b17d4e97f494247e18060e9c727fd16ce78ea

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Citations in the corpus

The performance of different propensity score methods for estimating marginal hazard ratios (2007, methodology): “This approach was included as it has been found to perform well compared with other commonly used alternatives [34].”
Evaluation of subset matching methods and forms of covariate balance (2016, methodology): “**We decided to fix the number of treated and control subjects, as in [14], instead of randomly assigning to treatment each unit by means of another Bernoulli random variable, as in [16], to have a fixed number of maximum possible pairs and be able to better compare the number of units matched acros”
Why Propensity Scores Should Not Be Used for Matching (2019, methodology): “Then, following the most common implementation of either PSM or MDM (see Austin 2009, p.173 and the content analysis below) we (greedily) match the first treated unit to the closest control; then, we match the second treated unit with the closest control unit that has not already been matched, and…”
A comparison of 12 algorithms for matching on the propensity score (2013, result): “Although prior studies have compared the relative performance of different caliper widths when using nearest neighbor matching within specified caliper widths [10, 11], there is a paucity of research comparing different matching algorithms.”
Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples (2009, background): “2 of the standard deviation of the logit of the propensity score [3, 17, 18].”
Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies (2010, neutral): “10) [27].”