"Handbook Of Matching And Weighting Adjustments For Causal Inference | Statistic

An observational study infers the effects caused by a treatment policy program intervention or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching weighting machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone matching and weighting do not use outcome information so they are part of the design of an observational study. When used in conjunction with models for the outcome matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine economics public health psychology epidemiology public program evaluation and statistics who examine evidence of the effects on human beings of treatments policies or exposures. |Handbook of Matching and Weighting Adjustments for Causal Inference | Statistics