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Image of - Handbook Of Matching And Weighting Adjustments For Causal Inference | Hardcover
Handbook Of Matching And Weighting Adjustments For Causal Inference | Hardcover

Handbook Of Matching And Weighting Adjustments For Causal Inference | Hardcover

by Zubizarreta J.R.

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.

Highlights

  • binding-icon

    9780367609528

    ISBN:

  • binding-icon

    Zubizarreta J.R.

    Author:

  • binding-icon

    634

    Pages:

  • binding-icon

    7500 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2023

    Year:

  • edition-icon

    1st Edition

    Edition:

  • binding-icon

    Hardcover

    Binding:

₹18347

₹22934

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.

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