What You Can Learn from Wrong Causal Models

  • Richard A. BerkEmail author
  • Lawrence Brown
  • Edward George
  • Emil Pitkin
  • Mikhail Traskin
  • Kai Zhang
  • Linda Zhao
Part of the Handbooks of Sociology and Social Research book series (HSSR)


It is common for social science researchers to provide estimates of causal effects from regression models imposed on observational data. The many problems with such work are well documented and widely known. The usual response is to claim, with little real evidence, that the causal model is close enough to the “truth” that sufficiently accurate causal effects can be estimated. In this chapter, a more circumspect approach is taken. We assume that the causal model is a substantial distance from the truth and then consider what can be learned nevertheless. To that end, we distinguish between how nature generated the data, a “true” model representing how this was accomplished, and a working model that is imposed on the data. The working model will typically be “wrong.” Nevertheless, unbiased or asymptotically unbiased estimates from parametric, semiparametric, and nonparametric working models can often be obtained in concert with appropriate statistical tests and confidence intervals. However, the estimates are not of the regression parameters typically assumed. Estimates of causal effects are not provided. Correlation is not causation. Nor is partial correlation, even when dressed up as regression coefficients. However, we argue that insights about causal effects do not require estimates of causal effects. We also discuss what can be learned when our alternative approach is not persuasive.


Census Tract Causal Effect Conditional Distribution Causal Inference Causal Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Richard A. Berk
    • 1
    Email author
  • Lawrence Brown
    • 1
  • Edward George
    • 1
  • Emil Pitkin
    • 1
  • Mikhail Traskin
    • 1
  • Kai Zhang
    • 1
  • Linda Zhao
    • 1
  1. 1.Departments of Statistics and CriminologyUniversity of PennsylvaniaPhiladelphiaUSA

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