Question #43013

can you help me by giving me a beriefe discription of propensity score maching model?
thank you

Expert's answer

In the statistical analysis of observational data, propensity score matching (PSM) is a statistical matching technique that

attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. PSM attempts to reduce the bias due to confounding to variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among units that received the treatment versus to those that did not. The technique was first published by Paul Rosenbaum and Donald Rubin in 1983, and implements the Rubin

causal model for observational studies.

The possibility of bias arises because the apparent difference in outcome between these two groups of units may depend on characteristics that affected whether or not a unit received a given treatment instead of due to the effect of the treatment. Inrandomized experiments, the randomization enables unbiased estimation of treatment effects; for each covariate, randomization implies that treatment-groups will be balanced on average, by the law of large numbers.

Unfortunately, for observational studies, the assignment of treatments to research subjects is, by definition, not randomized. Matching attempts to mimic randomization by creating a sample of units that received the treatment that is comparable on all observed covariates to a sample of units that did not receive the treatment.

attempts to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that predict receiving the treatment. PSM attempts to reduce the bias due to confounding to variables that could be found in an estimate of the treatment effect obtained from simply comparing outcomes among units that received the treatment versus to those that did not. The technique was first published by Paul Rosenbaum and Donald Rubin in 1983, and implements the Rubin

causal model for observational studies.

The possibility of bias arises because the apparent difference in outcome between these two groups of units may depend on characteristics that affected whether or not a unit received a given treatment instead of due to the effect of the treatment. Inrandomized experiments, the randomization enables unbiased estimation of treatment effects; for each covariate, randomization implies that treatment-groups will be balanced on average, by the law of large numbers.

Unfortunately, for observational studies, the assignment of treatments to research subjects is, by definition, not randomized. Matching attempts to mimic randomization by creating a sample of units that received the treatment that is comparable on all observed covariates to a sample of units that did not receive the treatment.

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