Causal Analysis: Assumptions, Models, and Data

Chester A. Schriesheim; Lawrence R. James; Stanley A. Muliak; Jeanne M. Brett

doi:10.2307/258244

January 1, 1984Academy of Management Review

Causal Analysis: Assumptions, Models, and Data

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Key Points

The review aims to summarize the key concepts presented in the book on causal analysis.
Reviewed the book's content focusing on assumptions, models, and data relevance.
Analyzed the application of causal analysis techniques in different contexts.
Identified essential assumptions necessary for effective causal modeling.
Highlighted various models used in causal analysis and their practical applications.

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Implication

Review discusses the foundational concepts of causal analysis in various fields, highlighting its significance.

Abstract

This article presents a review of the book “Causal Analysis: Assumptions, Models, and Data,” by Lawrence R. James, Stanley A. Mullak, and Jeanne M. Brett.

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Schriesheim et al. (1984) studied this question.

synapsesocial.com/papers/6a0a108d30285ee4a1342c2c https://doi.org/10.2307/258244

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Causal Analysis: Assumptions, Models, and Data

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Implication

Abstract

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Causal Analysis: Assumptions, Models, and Data

Key Points

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Implication

Abstract

Discussion

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