The employment of damage mitigation measures by individuals is an important component of integrated flood risk management. In order to promote efficient damage mitigation measures, accurate estimates of their damage mitigation potential are required. That is, for correctly assessing the damage mitigation measures’ effectiveness from survey data, one needs to control for sources of bias. A biase...

In many observational studies, researchers estimate treatment effects using propensity score matching techniques. Estimation of propensity scores are complicated when some values of the covariates are missing. We can use multiple imputation to create completed datasets, from which propensity scores can be computed; however, we may be sensitive to the accuracy of the imputation models. We propos...

Matching is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance actually has been obtained are provided. The underlying matching algorithm is written in C++, makes extensive use of system BLAS and scales efficien...

Propensity-score methods are increasingly being used to reduce the impact of treatment-selection bias in the estimation of treatment effects using observational data. Commonly used propensity-score methods include covariate adjustment using the propensity score, stratification on the propensity score, and propensity-score matching. Empirical and theoretical research has demonstrated that matchi...

ABSTRACT Propensity score approaches in outcomes and epidemiological research are most often used for sample selection by matching, analysis of causal effect by stratification, or risk adjustment by combining propensity score and regression models. Several computing tools are available including SAS, S-PLUS/R, and SPSS to develop and implement propensity score approaches in a variety of applica...

Dempster-Shafer Theory is specially advantaged in information fusion, while Support Vector Machine (SVM) can well deal with high-dimensional limited sample data. This Article firstly forecasts the data samples by categories with multiple SVMs, and hence based thereon, fuses the resulting information from multiple SVM models by using DS theory. At the end, Anderson's Iris data set is used to sim...

Selective prescribing of conventional antipsychotic medication (APM) to frailer patients is thought to have led to overestimation of the association with mortality in pharmacoepidemiologic studies relying on claims data. The authors assessed the validity of different analytic techniques to address such confounding. The cohort included 82,012 persons initiating APM use after admission to a nursi...

Understanding the role of foreign aid in poverty alleviation is one of the central inquiries for development economics. To augment past crosscountry studies and randomized evaluations, this project estimates the first sub-national model of foreign aid allocation and impact. Newly geocoded aid project data from Malawi is used in combination with multiple rounds of living standards data to predic...

Theorists argue that deliberation promotes enlightenment and consensus, but scholars do not know how deliberation affects policy opinions. Using the deliberative democracy and public opinion literatures as a guide, I develop a theory of opinion updating where citizens who deliberate revise their prior beliefs, particularly when they encounter consensual messages. A key aspect of this model is t...

In observational studies, investigators have no control over the treatment assignment. The treated and non-treated (that is, control) groups may have large differences on their observed covariates, and these differences can lead to biased estimates of treatment effects. Even traditional covariance analysis adjustments may be inadequate to eliminate this bias. The propensity score, defined as th...