1932

Abstract

We describe and contrast two distinct problem areas for statistical causality: studying the likely effects of an intervention (effects of causes) and studying whether there is a causal link between the observed exposure and outcome in an individual case (causes of effects). For each of these, we introduce and compare various formal frameworks that have been proposed for that purpose, including the decision-theoretic approach, structural equations, structural and stochastic causal models, and potential outcomes. We argue that counterfactual concepts are unnecessary for studying effects of causes but are needed for analyzing causes of effects. They are, however, subject to a degree of arbitrariness, which can be reduced, though not in general eliminated, by taking account of additional structure in the problem.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-070121-061120
2022-03-07
2024-04-23
Loading full text...

Full text loading...

/deliver/fulltext/statistics/9/1/annurev-statistics-070121-061120.html?itemId=/content/journals/10.1146/annurev-statistics-070121-061120&mimeType=html&fmt=ahah

Literature Cited

  1. Angrist J, Imbens G, Rubin DB. 1996. Identification of causal effects using instrumental variables. J. Am. Stat. Assoc. 91:444–55
    [Google Scholar]
  2. Balke AA, Pearl J. 1997. Bounds on treatment effects from studies with imperfect compliance. J. Am. Stat. Assoc. 92:1172–76
    [Google Scholar]
  3. Beyea J, Greenland S. 1999. The importance of specifying the underlying biologic model in estimating the probability of causation. Health Phys. 76:269–74
    [Google Scholar]
  4. Bowden RJ, Turkington DA. 1984. Instrumental Variables Cambridge, UK: Cambridge Univ. Press
  5. Bühlmann P. 2020. Invariance, causality and robustness. Stat. Sci. 35:40426
    [Google Scholar]
  6. Constantinou P, Dawid AP. 2017. Extended conditional independence and applications in causal inference. Ann. Stat. 45:2618–53
    [Google Scholar]
  7. Corradi F, Musio M. 2020. Causes of effects via a Bayesian model selection procedure. J. R. Stat. Soc. Ser. A 183:1777–92
    [Google Scholar]
  8. Cowell RG, Dawid AP, Lauritzen SL, Spiegelhalter DJ. 1999. Probabilistic Networks and Expert Systems New York: Springer
  9. Cox LA. 1984. Probability of causation and the attributable proportion of risk. Risk Anal. 4:221–30
    [Google Scholar]
  10. Cuellar M. 2017. Causal reasoning and data analysis in the law: definition, estimation, and usage of the probability of causation. PhD Thesis Dep. Stat. and Heinz Coll. Public Policy Carnegie Mellon Univ. Pittsburgh, PA:
    [Google Scholar]
  11. Dawid AP. 1976. Properties of diagnostic data distributions. Biometrics 32:647–58
    [Google Scholar]
  12. Dawid AP. 1979. Conditional independence in statistical theory (with discussion). J. R. Stat. Soc. B 41:1–31
    [Google Scholar]
  13. Dawid AP. 2000. Causal inference without counterfactuals (with discussion). J. Am. Stat. Assoc. 95:407–48
    [Google Scholar]
  14. Dawid AP 2003. Causal inference using influence diagrams: the problem of partial compliance (with discussion). Highly Structured Stochastic Systems PJ Green, NL Hjort, S Richardson 45–81 Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  15. Dawid AP. 2010. Beware of the DAG!. J. Mach. Learn. Res. 6:59–86
    [Google Scholar]
  16. Dawid AP. 2013. Vote of thanks on “A Bayesian approach to complex clinical diagnoses: a case-study in child abuse. ,” by Nicky Best, Deborah Ashby, Frank Dunstan, David Foreman and Neil McIntosh J. R. Stat. Soc. Ser. A 176:83–84
    [Google Scholar]
  17. Dawid AP. 2015. Statistical causality from a decision-theoretic perspective. Annu. Rev. Stat. Appl. 2:273–303
    [Google Scholar]
  18. Dawid AP. 2017. On individual risk. Synthese 194:3445–74
    [Google Scholar]
  19. Dawid AP. 2021. Decision-theoretic foundations for statistical causality. J. Causal Inference 9:39–77
    [Google Scholar]
  20. Dawid AP. 2022. The tale wags the DAG. Probabilistic and Causal Inference: The Works of Judea Pearl R Dechter, H Geffner, J Halpern New York: ACM. In press
    [Google Scholar]
  21. Dawid AP, Didelez V. 2012. “Imagine a can opener”—the magic of principal stratum analysis. Int. J. Biostat. 8:119
    [Google Scholar]
  22. Dawid AP, Faigman DL, Fienberg SE. 2014. Fitting science into legal contexts: assessing effects of causes or causes of effects? (with discussion and authors' rejoinder). Sociol. Methods Res. 43:359–421
    [Google Scholar]
  23. Dawid AP, Faigman DL, Fienberg SE. 2015. On the causes of effects: response to Pearl. Sociol. Methods Res. 44:165–74
    [Google Scholar]
  24. Dawid AP, Humphreys M, Musio M. 2022. Bounding causes of effects with mediators. Sociol. Methods Res. In press
    [Google Scholar]
  25. Dawid AP, Murtas R, Musio M 2016a. Bounding the probability of causation in mediation analysis. Topics on Methodological and Applied Statistical Inference TD Battista, E Moreno, W Racugno 75–84 New York: Springer
    [Google Scholar]
  26. Dawid AP, Musio M 2022. What can group level data tell us about individual causality?. Statistics in the Public Interest A Carriquiry, J Tanur, W Eddy New York: Springer. In press
    [Google Scholar]
  27. Dawid AP, Musio M, Fienberg SE. 2016b. From statistical evidence to evidence of causality. Bayesian Anal. 11:725–52
    [Google Scholar]
  28. Dawid AP, Musio M, Murtas R. 2017. The probability of causation. Law Probab. Risk 16:163–79
    [Google Scholar]
  29. Faigman DL, Monahan J, Slobogin C. 2014. Group to individual (G2i) inference in scientific expert testimony. Univ. Chicago Law Rev. 81:417–80
    [Google Scholar]
  30. Goldberg R 2011. Perspectives on Causation Oxford, UK: Hart
  31. Greenland S. 1999. Relation of probability of causation to relative risk and doubling dose: a methodologic error that has become a social problem. Am. J. Public Health 89:1166–69
    [Google Scholar]
  32. Halpern JY. 2016. Actual Causality Cambridge, MA: MIT Press
  33. Hart HLA, Honoré AM. 1985. Causation in the Law Oxford, UK: Clarendon:
  34. Hausman D. 1998. Causal Asymmetries Cambridge, UK: Cambridge Univ. Press
  35. Hempstead v. Pfizer, Inc., 150 F. Suppl. 3d 644 (D.S.C. 2015 )
  36. Hill AB. 1965. The environment and disease: association or causation?. Proc. R. Soc. Med. 58:295–300
    [Google Scholar]
  37. Holland PW. 1986. Statistics and causal inference (with discussion). J. Am. Stat. Assoc. 81:945–70
    [Google Scholar]
  38. Holland PW. 1988. Causal inference, path analysis, and recursive structural equations models. Sociol. Methodol. 18:449–84
    [Google Scholar]
  39. Honoré AM. 2010. Causation in the law. The Stanford Encyclopedia of Philosophy (Winter 2010 Edition) EN Zalta. Stanford, CA: Stanford Univ. Metaphys. Res. Lab. https://plato.stanford.edu/archives/win2010/entries/causation-law/
    [Google Scholar]
  40. Imbens GW, Angrist J. 1994. Identification and estimation of local average treatment effects. Econometrica 62:467–76
    [Google Scholar]
  41. Katan MB. 1986. Apolipoprotein E isoforms, serum cholesterol, and cancer. Lancet 327:8479507–8
    [Google Scholar]
  42. Kuroki M, Cai Z. 2011. Statistical analysis of ‘probabilities of causation’ using co-variate information. Scand. J. Stat. 38:564–77
    [Google Scholar]
  43. Lauritzen SL, Dawid AP, Larsen BN, Leimer HG. 1990. Independence properties of directed Markov fields. Networks 20:491–505
    [Google Scholar]
  44. Lewis DK. 1973. Counterfactuals Oxford, UK: Blackwell
  45. Mackie JL. 1980. The Cement of the Universe: A Study of Causation Oxford, UK: Oxford Univ. Press
  46. Mill JS. 1843. A System of Logic, Ratiocinative and Inductive: Being a Connected View of the Principles of Evidence, and Methods of Scientific Investigation London: John W. Harper
  47. Pearl J. 2009. Causality: Models, Reasoning and Inference Cambridge, UK: Cambridge Univ. Press. , 2nd ed..
  48. Pearl J. 2015. Causes of effects and effects of causes. Sociol. Methods Res. 44:149–64
    [Google Scholar]
  49. Pearl J, Mackenzie D 2018. The Book of Why New York: Basic Books
  50. Price H. 1991. Agency and probabilistic causality. Br. J. Philos. Sci. 42:157–76
    [Google Scholar]
  51. Reichenbach H. 1956. The Direction of Time Berkeley: Univ. Calif. Press
  52. Robins JM, Greenland S. 1989. The probability of causation under a stochastic model for individual risk. Biometrics 45:1125–38
    [Google Scholar]
  53. Rubin DB. 1974. Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66:688–701
    [Google Scholar]
  54. Sanders J, Faigman DL, Imrey PB, Dawid AP. 2021. Differential etiology: inferring specific causation in the law from group data in science. Arizona Law Rev. 63:851922
    [Google Scholar]
  55. Tian J, Pearl J. 2000. Probabilities of causation: bounds and identification. 28:287–313
    [Google Scholar]
  56. Verma T, Pearl J. 1990. Causal networks: Semantics and expressiveness. In Machine Intelligence and Pattern RecognitionVol. 9ed. RD Shachter, TS Levitt, LN Kanal, JF Lemmerpp. 6976 Amsterdam: Elsevier
    [Google Scholar]
  57. Woodward J. 2003. Making Things Happen: A Theory of Causal Explanation Oxford, UK: Oxford Univ. Press
  58. Woodward J. 2016. Causation and manipulability. The Stanford Encyclopedia of Philosophy EN Zalta. Stanford, CA: Stanford Univ. Metaphys. Res. Lab https://plato.stanford.edu/entries/causation-mani/
    [Google Scholar]
  59. Wright SS. 1921. Correlation and causation. J. Agric. Res. 20:557–85
    [Google Scholar]
/content/journals/10.1146/annurev-statistics-070121-061120
Loading
/content/journals/10.1146/annurev-statistics-070121-061120
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error