Skip to main content

Confounding and Confounder Control

  • Reference work entry
  • First Online:
International Encyclopedia of Statistical Science

Introduction

The word confounding has been used to refer to at least three distinct concepts. In the oldest and most widespread usage, confounding is a source of bias in estimating causal effects. This bias is sometimes informally described as a mixing of effects of extraneous factors (called confounders) with the effect of interest, and important in causal inference (see Causation and Causal Inference). This usage predominates in nonexperimental research, especially in epidemiology and sociology. In a second and more recent usage originating in statistics, confounding is a synonym for a change in an effect measure upon stratification or adjustment for extraneous factors (a phenomenon called noncollapsibility or Simpson’s paradox;see Simpson’s Paradox; Collapsibility). In a third usage, originating in the experimental-design literature, confounding refers to inseparability of main effects and interactions under a particular design (see Interaction).

The three concepts are closely...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 1,100.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References and Further Reading

  • Bang H, Robins J (2005) Doubly robust estimation in missing data and causal inference models. Biometrics 61:962–972

    MATH  MathSciNet  Google Scholar 

  • Cox DR (1958) Planning of experiments. Wiley, New York

    MATH  Google Scholar 

  • Fisher RA (1935) The Design of experiments. Oliver & Boyd, Edinburgh

    Google Scholar 

  • Glymour MM, Greenland S (2008) Causal diagrams. In: Rothman KJ, Greenland S, Lash TL (eds) Modern epidemiology, 3rd edn. Lippincott, Philadelphia, pp 183–209

    Google Scholar 

  • Greenland S (1996) Absence of confounding does not correspond to collapsibility of the rate ratio or rate difference. Epidemiology 7:498–501

    Google Scholar 

  • Greenland S (2000) When should epidemiologic regressions use random coefficients? Biometrics 56:915–921

    MATH  Google Scholar 

  • Greenland S (2003) Quantifying biases in causal models. Epidemiology 14:300–307

    Google Scholar 

  • Greenland S (2008) Variable selection and shrinkage in the control of multiple confounders. Am J Epidemiol 167:523–529. Erratum 1142

    Google Scholar 

  • Greenland S, Neutra RR (1980) Control of confounding in the assessment of medical technology. Int J Epidemiol 9:361–367

    Google Scholar 

  • Greenland S, Robins JM (1986) Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol 15:413–419

    Google Scholar 

  • Greenland S, Robins JM (2009) Identifiability, exchangeability, and confounding revisited. Epidemiol Perspect Innov (online journal) 6:4

    Google Scholar 

  • Greenland S, Robins JM, Pearl J (1999a) Confounding and collapsibility in causal inference. Stat Sci 14:29–46

    MATH  Google Scholar 

  • Greenland S, Pearl J, Robins JM (1999b) Causal diagrams for epidemiologic research. Epidemiology 10:37–48

    Google Scholar 

  • Greenland S, Rothman KJ, Lash TL (2008) Measures of effect and measures of association. In: Rothman KJ, Greenland S, Lash TL (eds) Modern epidemiology, 3rd edn. Lippincott, Philadelphia, pp 51–70

    Google Scholar 

  • Hernán M, Hernandez-Diaz S, Werler MM, Mitchell AA (2002) Causal knowledge as a prerequisite for confounding evaluation. Am J Epidemiol 155:176–184

    Google Scholar 

  • Hirano K, Imbens G, Ridder G (2003) Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71:1161–1189

    MATH  MathSciNet  Google Scholar 

  • Maldonado G, Greenland S (1993) A simulation study of confounder-selection strategies. Am J Epidemiol 138: 923–936

    Google Scholar 

  • Mill JS (1843) A system of logic, ratiocinative and inductive. Reprinted by Longmans. Green & Company, London, 1956

    Google Scholar 

  • Pearl J (1995) Causal diagrams for empirical research. Biometrika 82:669–710

    MATH  MathSciNet  Google Scholar 

  • Pearl J (2009) Causality, 2nd edn. Cambridge University Press, New York

    MATH  Google Scholar 

  • Robins JM (1998) Correction for non-compliance in equivalence trials. Stat Med 17:269–302

    Google Scholar 

  • Robins JM (2001) Data, design, and background knowledge in etiologic inference. Epidemiology 12:313–320

    Google Scholar 

  • Robins JM, Morgenstern H (1987) The foundations of confounding in epidemiology. Comput Math Appl 14:869–916

    MATH  MathSciNet  Google Scholar 

  • Rothman KJ (1977) Epidemiologic methods in clinical trials. Cancer 39:1771–1775

    Google Scholar 

  • Rubin DB (1991) Practical implications of modes of statistical inference for causal effects and the critical role of the assignment mechanism. Biometrics 47:1213–1234

    MATH  MathSciNet  Google Scholar 

  • Stone R (1993) The assumptions on which causal inference rest. J R Stat Soc B 55:455–466

    MATH  Google Scholar 

  • Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc B 58:267–288

    MATH  MathSciNet  Google Scholar 

  • Yule GU (1903) Notes on the theory of association of attributes in statistics. Biometrika 2:121–134

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this entry

Cite this entry

Greenland, S. (2011). Confounding and Confounder Control. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_184

Download citation

Publish with us

Policies and ethics