On the Estimation Accuracy of Causal Effects using Supplementary Variables

DOI10.1111/sjos.12188zbMath1384.62235OpenAlexW2239835926MaRDI QIDQ2815595

Takahiro Hayashi, Manabu Kuroki

Publication date: 29 June 2016

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/sjos.12188

zbMATH Keywords

multicollinearityintermediate variabletotal effectcausal risk differencerecursive regression model

Mathematics Subject Classification ID

Multivariate analysis (62H99) Linear regression; mixed models (62J05)

Related Items (2)

The Identification of Direct and Indirect Effects in Studies with an Unmeasured Intermediate Variable ⋮ Exact variance formula for the estimated mean outcome with external intervention based on the front-door criterion in Gaussian linear structural equation models

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian inference for causal effects: The role of randomization
• SELECTION OF POST-TREATMENT VARIABLES FOR ESTIMATING TOTAL EFFECT FROM EMPIRICAL RESEARCH
• Measurement bias and effect restoration in causal inference
• Graphical identifiability criteria for causal effects in studies with an unobserved treatment/response variable
• Bounds on Direct Effects in the Presence of Confounded Intermediate Variables
• A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect

This page was built for publication: On the Estimation Accuracy of Causal Effects using Supplementary Variables