Psm attempts to reduce the bias due to confounding variables that could be found in an estimate of the treatment effect.
Propensity score matching diagram.
Hirano k and imbens gw.
Propensity score matching is a statistical matching technique that attempts to estimate the effect of a treatment e g intervention by accounting for the factors that predict whether an individual would be eligble for receiving the treatment the wikipedia page provides a good example setting.
Say we are interested in the effects of smoking on health.
Jm oakes and js kaufman jossey bass san francisco ca.
An alternative method of controlling for observed variables is propensity score matching.
Propensity score matching is a new way to predict marketing decisions.
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.
Logistical regression isn t.
The score is a predicted probability that students receive a treatment given their observed characteristics.
Propensity scores are usually computed using logistic regression with group treatment status regressed on observed baseline characteristics including age gender and behaviors of relevance to the research.
Propensity score matching for social epidemiology in methods in social epidemiology eds.
Using propensity score matching.
Researchers first estimate a propensity score for each student or other unit in the sample rosenbaum and rubin 1983.