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Propensity score
In the analysis of treatment effects, suppose that we have a binary treatment T, an outcome Y, and background variables X. The propensity score is defined as the conditional probability of treatment given background variables: Additional recommended knowledgeThe propensity score was introduced by Rosenbaum and Rubin (1983) to provide an alternative method for estimating treatment effects when treatment assignment is not random, but can be assumed to be unconfounded. Let Y(0) and Y(1) denote the potential outcomes under control and treatment, respectively. Then treatment assignment is (conditionally) unconfounded if treatment is independent of potential outcomes conditional on X. This can be written compactly as where Rosenbaum and Rubin showed that if unconfoundedness holds, then While it is cognitively impossible to use the definition above for determining whether unconfoundedness holds in any specific situation, Pearl (2000) has shown that a simple graphical criterion called backdoor provides an equivalent definition of unconfoundedness. References
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This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Propensity_score". A list of authors is available in Wikipedia. |