In this paper, we present a novel framework for automatically assessing facial attractiveness that considers four ratio feature sets as objective elements of facial attractiveness. In our framework, these feature sets are combined with three regression-based predictors to estimate a facial beauty score. To enhance the system’s performance to make it comparable with human scoring, we apply a sco...

We show that relative mean survival parameters of a semiparametric log-linear model can be estimated using covariate data from an incident sample and a prevalent sample, even when there is no prospective follow-up to collect any survival data. Estimation is based on an induced semiparametric density ratio model for covariates from the two samples, and it shares the same structure as for a logis...

The use of propensity scores to adjust for measured confounding factors has become increasingly popular in cohort studies. However, their use in case-control and case-cohort studies has received little attention. The authors present some theory on the estimation and use of propensity scores in case-control and case-cohort studies and present the results of simulation studies that examine whethe...

The propensity score is the conditional probability of exposure to a treatment given observed covariates. In a cohort study, matching or stratifying treated and control subjects on a single variable, the propensity score, tends to balance all of the observed covariates; however, unlike random assignment of treatments, the propensity score may not also balance unobserved covariates. The authors ...

BACKGROUND The scientific evidence-base for policies to tackle health inequalities is limited. Natural policy experiments (NPE) have drawn increasing attention as a means to evaluating the effects of policies on health. Several analytical methods can be used to evaluate the outcomes of NPEs in terms of average population health, but it is unclear whether they can also be used to assess the outc...

Propensity score methods are being increasingly used as a less parametric alternative to traditional regression to balance observed differences across groups in both descriptive and causal comparisons. Data collected in many disciplines often have analytically relevant multilevel or clustered structure. The propensity score, however, was developed and has been used primarily with unstructured d...

Much of the work on propensity score analysis has focused on the case where the treatment is binary. In this chapter we examine an extension to the propensity score method, in a setting with a continuous treatment. Following Rosenbaum and Rubin (1983) and most of the other literature on propensity score analysis, we make an unconfoundedness or ignorability assumption, that adjusting for differe...