• Yuzo HosoyaEmail author
  • Kosuke Oya
  • Taro Takimoto
  • Ryo Kinoshita
Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)


In advance of focusing in subsequent chapters on the main theme of the measures of interdependency, Chap. 1 provides a brief overview of the literature on empirical causal analysis and places the theme in a broader perspective, comparing a variety of conflicting views on how certain statistical associations can be viewed as causal. Among others, alluded to is the field experiment model of detecting causal effects by Neyman (1923) and its reliance on a counterfactual assumption. Controlled random experiments are compared with observational studies in econometrics. The concepts of causality and exogeneity in the framework of the simultaneous equation are discussed. Specifically, ancillarity and conditioning in statistical inferences are explained and their relation to exogeneity is expounded. A preliminary concept of Granger causality is introduced, and the role of prediction improvement in empirical analyses is emphasized.


Ancillarity Concept of causality Conditional inference Controlled random experiment Cowles approach Endogenous variable Exogenous variable Granger causality Marshall’s causal effect Neyman model of field experiment Structural equation model 


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Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Yuzo Hosoya
    • 1
    Email author
  • Kosuke Oya
    • 2
  • Taro Takimoto
    • 3
  • Ryo Kinoshita
    • 4
  1. 1.Tohoku UniversitySendaiJapan
  2. 2.Osaka UniversityToyonakaJapan
  3. 3.Kyushu UniversityFukuokaJapan
  4. 4.Tokyo Keizai UniversityKokubunjiJapan

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