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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]
One very early weighted estimator is the Horvitz–Thompson estimator of the mean. [3] When the sampling probability is known, from which the sampling population is drawn from the target population, then the inverse of this probability is used to weight the observations. This approach has been generalized to many aspects of statistics under ...
Covariate adjustment can be carried out in a variety of ways. Gordon et al. (2018) illustrate many of these methods by means of online advertising data, such as propensity score matching, stratification, regression adjustment, and inverse probability weighted regression adjustment. They find that despite great variation in variables within ...
Morisita's overlap index; Morris method; ... Propensity score; Propensity score matching; ... Weighted covariance matrix – redirects to Sample mean and sample ...
The propensity theory of probability is a probability interpretation in which the probability is thought of as a physical propensity, disposition, or tendency of a given type of situation to yield an outcome of a certain kind, or to yield a long-run relative frequency of such an outcome. [1]
The Australian breakdancer, 37, who captured international attention due to her viral Olympics routine that received a 0 score from the judges, recently appeared on Australia's Today morning show ...
Henry's [26] proposes an extended model-assisted weighting design-effect measure for single-stage sampling and calibration weight adjustments for a case where = + +, where is a vector of covariates, the model errors are independent, and the estimator of the population total is the general regression estimator (GREG) of Särndal, Swensson, and ...