Probabilistic Analysis of Balancing Scores for Causal Inference
2015 (English)In: Journal of Mathematics Research, ISSN 1916-9795, E-ISSN 1916-9809, Vol. 7, no 2, 90-100 p.Article in journal (Refereed) Published
Propensity scores are often used for stratification of treatment and control groups of subjects in observational data to remove confounding bias when estimating of causal effect of the treatment on an outcome in so-called potential outcome causal modeling framework. In this article, we try to get some insights into basic behavior of the propensity scores in a probabilistic sense. We do a simple analysis of their usage confining to the case of discrete confounding covariates and outcomes. While making clear about behavior of the propensity score our analysis shows how the so-called prognostic score can be derived simultaneously. However the prognostic score is derived in a limited sense in the current literature whereas our derivation is more general and shows all possibilities of having the score. And we call it outcome score. We argue that application of both the propensity score and the outcome score is the most efficient way for reduction of dimension in the confounding covariates as opposed to current belief that the propensity score alone is the most efficient way.
Place, publisher, year, edition, pages
Canada: Canadian Center of Science and Education , 2015. Vol. 7, no 2, 90-100 p.
causal effects, confounding, potential outcome causal model, causal graphical model
Research subject Statistics
IdentifiersURN: urn:nbn:se:umu:diva-103611DOI: 10.5539/jmr.v7n2p90OAI: oai:DiVA.org:umu-103611DiVA: diva2:813759
ProjectsBayesian causal graphical models for prediction of unemployment persistence
FunderForte, Swedish Research Council for Health, Working Life and Welfare, 2010-0592