English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Although propensity scores have been central to the estimation of causaleffects for over 30 years, only recently has the statistical literature begunto consider in detail methods for Bayesian estimation of propensity scores andcausal effects. Underlying this recent body of literature on Bayesianpropensity score estimation is an implicit discordance between the goal of thepropensity score and the use of Bayes theorem. The propensity score condensesmultivariate covariate information into a scalar to allow estimation of causaleffects without specifying a model for how each covariate relates to theoutcome. Avoiding specification of a detailed model for the outcome responsesurface is valuable for robust estimation of causal effects, but this strategyis at odds with the use of Bayes theorem, which presupposes a full probabilitymodel for the observed data. The goal of this paper is to explicate thisfundamental feature of Bayesian estimation of causal effects with propensityscores in order to provide context for the existing literature and for futurework on this important topic.

The central role of Bayes theorem for joint estimation of causal effects and propensity scores