Estimating causal effects from incomplete data requires additional and inherently untestable assumptions regarding the mechanism giving rise to the missing data. We show that using causal diagrams to represent these additional assumptions both complements and clarifies some of the central issues in missing data theory, such as Rubin's classification of missingness mechanisms (as missing complet...

Species distribution models (SDMs) are increasingly used in ecology, biogeography, and wildlife management to learn about the species–habitat relationships abundance across space time. Distance sampling (DS) capture-recapture (CR) two widely collected data types abundance; still, they seldomly SDMs due lack of spatial coverage. However, fusion sources can increase coverage, which reduce paramet...

A cure for prostate cancer (CaP) will be possible only after a complete understanding of the mechanisms causing this disease to progress from androgen dependence to androgen independence. To carry on a careful characterization of the phenotypes of CaP cell lines before and after acquisition of androgen independence, we used two human CaP LNCaP sublines: LNCaP(nan), which is androgen dependent (...

2. APPLICATION TO NHANES: CORRECTION OF VARIABLE DEFINITION The main manuscript stated that loge(average no. drinks per day + 1) was used as covariate in the model of interest in the analysis of data from NHANES. This was incorrect: loge(average no. drinks per day) was used as covariate. For the ACC and MI estimators, the number of drinks minus one was imputed using negative binomial regression...

Data completeness is one of the most common challenges that hinder performance data analytics platforms. Different studies have assessed effect missing values on different classification models based a single evaluation metric, namely, accuracy. However, accuracy its own misleading measure classifier because it does not consider unbalanced datasets. This paper presents an experimental study ass...