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Propensity scores are used to reduce confounding by equating groups based on these covariates. Suppose that we have a binary treatment indicator Z, a response variable r, and background observed covariates X. The propensity score is defined as the conditional probability of treatment given background variables:
[1] [2] [3] Propensity score matching, an early matching technique, was developed as part of the Rubin causal model, [4] but has been shown to increase model dependence, bias, inefficiency, and power and is no longer recommended compared to other matching methods. [5]
propensity score matching or weighting; instrumental variables; Panel analysis; Of all of these designs, the regression discontinuity design comes the closest to the experimental design, as the experimenter maintains control of the treatment assignment and it is known to "yield an unbiased estimate of the treatment effects".
These may include methods such as post-stratification, raking, or propensity score (estimation) models - used to perform an adjustment of the sample to some known (or estimated) strata sizes. These adjustments can be in addition of design weights , which aims to account for imbalances due to some known sampling design.
This page was last edited on 5 September 2012, at 21:23 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
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An alternative estimator is the augmented inverse probability weighted estimator (AIPWE) combines both the properties of the regression based estimator and the inverse probability weighted estimator. It is therefore a 'doubly robust' method in that it only requires either the propensity or outcome model to be correctly specified but not both.