Handbook of Matching and Weighting Adjustments for Causal Inference (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)
By: and and and
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- Synopsis
- 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.
- Copyright:
- 2023
Book Details
- Book Quality:
- Publisher Quality
- Book Size:
- 634 Pages
- ISBN-13:
- 9781000850819
- Related ISBNs:
- 9780367609535, 9781003102670, 9780367609528, 9780367609535, 9781003102670, 9780367609528
- Publisher:
- CRC Press
- Date of Addition:
- 04/13/23
- Copyrighted By:
- N/A
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Mathematics and Statistics
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
- Edited by:
- José R. Zubizarreta
- Edited by:
- Elizabeth A. Stuart
- Edited by:
- Dylan S. Small
- Edited by:
- Paul R. Rosenbaum
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- by José R. Zubizarreta
- by Elizabeth A. Stuart
- by Dylan S. Small
- by Paul R. Rosenbaum
- in Nonfiction
- in Mathematics and Statistics