1932

Abstract

We present an overview of the decision-theoretic framework of statistical causality, which is well suited for formulating and solving problems of determining the effects of applied causes. The approach is described in detail, and it is related to and contrasted with other current formulations, such as structural equation models and potential responses. Topics and applications covered include confounding, the effect of treatment on the treated, instrumental variables, and dynamic treatment strategies.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-010814-020105
2015-04-10
2024-10-13
Loading full text...

Full text loading...

/deliver/fulltext/statistics/2/1/annurev-statistics-010814-020105.html?itemId=/content/journals/10.1146/annurev-statistics-010814-020105&mimeType=html&fmt=ahah

Literature Cited

  1. Balke AA, Pearl J. 1997. Bounds on treatment effects from studies with imperfect compliance. J. Am. Stat. Assoc. 92:1172–76 [Google Scholar]
  2. Berzuini C, Dawid AP, Bernardinelli L. 2012a. Causality: Statistical Perspectives and Applications Chichester, UK: John Wiley & Sons [Google Scholar]
  3. Berzuini C, Dawid AP, Bernardinelli L. 2012b. An overview of statistical causality. See Berzuini et al. 2012a xvii–xxv
  4. Berzuini C, Dawid AP, Didelez V. 2012c. Assessing dynamic treatment strategies. See Berzuini et al. 2012a, chapter 8 85–100
  5. Chakraborty B, Murphy SA. 2014. Dynamic treatment regimes. Annu. Rev. Stat. Appl. 1:447–64 [Google Scholar]
  6. Constantinou P. 2013. Conditional independence and applications in statistical causality. PhD Thesis, University of Cambridge, Cambridge, UK [Google Scholar]
  7. Dawid AP. 1979. Conditional independence in statistical theory. J. R. Stat. Soc. B 41:1–15; discussion16–31 [Google Scholar]
  8. Dawid AP. 1980. Conditional independence for statistical operations. Ann. Stat. 8:598–617 [Google Scholar]
  9. Dawid AP. 2000. Causal inference without counterfactuals. J. Am. Stat. Assoc. 95:407–24; discussion424–48 [Google Scholar]
  10. Corrigenda 2002. Int. Stat. Rev. 70437 [Google Scholar]
  11. 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]
  12. Dawid AP. 2007a. Counterfactuals, hypotheticals and potential responses: A philosophical examination of statistical causality. Causality and Probability in the Sciences Texts in Philosophy 5 F Russo, J Williamson 503–32 London: College Publications [Google Scholar]
  13. Dawid AP. 2007b. Fundamentals of statistical causality. Research Report 279, Department of Statistical Science, University College London. 94 http://www.ucl.ac.uk/statistics/research/pdfs/rr279.pdf
  14. Dawid AP. 2010a. Beware of the DAG!. J. Mach. Learn. Res. Workshop Conf. Proc. 6:59–86 http://tinyurl.com/33va7tm [Google Scholar]
  15. Dawid AP. 2010b. Seeing and doing: the Pearlian synthesis. Heuristics, Probability and Causality: A Tribute to Judea Pearl R Dechter, H Geffner, JY Halpern 305–29 London: College Publications [Google Scholar]
  16. Dawid AP. 2011. The role of scientific and statistical evidence in assessing causality. Perspectives on Causation R Goldberg 133–47 Oxford, UK: Hart Publishing [Google Scholar]
  17. Dawid AP. 2012. The decision-theoretic approach to causal inference. See Berzuini et al. 2012a 25–42
  18. Dawid AP, Constantinou P. 2014. A formal treatment of sequential ignorability. Stat. Biosci. 6:166–88 [Google Scholar]
  19. Dawid AP, Didelez V. 2008. Identifying optimal sequential decisions. Proc. Twenty-Fourth Conf. Uncertain. Artif. Intell. D McAllester, P Myllymaki 113–20 Corvallis, OR: AUAI Press http://uai2008.cs.helsinki.fi/UAI_camera_ready/dawid.pdf [Google Scholar]
  20. Dawid AP, Didelez V. 2010. Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview. Stat. Surv. 4:184–231 [Google Scholar]
  21. Dawid AP, Didelez V. 2012. “Imagine a can opener”—the magic of principal stratum analysis. Int. J. Biostat. 8(1):Article 19 [Google Scholar]
  22. Dawid AP, Evett IW. 1997. Using a graphical method to assist the evaluation of complicated patterns of evidence. J. Forensic Sci. 42:226–31 [Google Scholar]
  23. Dawid AP, Faigman DL, Fienberg SE. 2013. Fitting science into legal contexts: Assessing effects of causes or causes of effects?. Sociol. Methods Res. 43:359–90; discussion391–421 [Google Scholar]
  24. Dawid AP, Musio M, Fienberg SE. 2014. From statistical evidence to evidence of causality. arXiv:1311.7513 [math.ST]
  25. Didelez V, Dawid AP, Geneletti SG. 2006. Direct and indirect effects of sequential treatments. Proc. Twenty-Second Conf. Uncertain. Artif. Intell. R Dechter, T Richardson 138–46 Arlington, VA: AUAI Press [Google Scholar]
  26. Frangakis CE, Rubin DB. 2002. Principal stratification in causal inference. Biometrics 58:21–29 [Google Scholar]
  27. Frydenberg M. 1990. The chain graph Markov property. Scand. J. Stat. 17:333–53 [Google Scholar]
  28. Geneletti S, Dawid AP. 2011. Defining and identifying the effect of treatment on the treated. Causality in the Sciences PM Illari, F Russo, J Williamson 728–49 Oxford, UK: Oxford Univ. Press [Google Scholar]
  29. Glymour C, Cooper GF. 1999. Computation, Causation and Discovery Menlo Park, CA: AAAI Press [Google Scholar]
  30. Guo H, Dawid AP. 2010. Sufficient covariates and linear propensity analysis. J. Mach. Learn. Res. Workshop Conf. Proc. 9:281–88 http://jmlr.csail.mit.edu/proceedings/papers/v9/guo10a/guo10a.pdf [Google Scholar]
  31. Hausman DM. 1998. Causal Asymmetries Cambridge, UK: Cambridge Univ. Press [Google Scholar]
  32. Holland PW. 1986. Statistics and causal inference. J. Am. Stat. Assoc. 81:945–60; discussion960–70 [Google Scholar]
  33. Huang Y, Valtorta M. 2006. Pearl's calculus of intervention is complete. Proc. Twenty-Second Conf. Uncertain. Artif. Intell. R Dechter, T Richardson 217–24 Arlington, VA: AUAI Press [Google Scholar]
  34. Lauritzen SL. 2000. Causal inference from graphical models. Complex Stochastic Systems OE Barndorff-Nielsen, DR Cox, C Klüppelberg 63–107 London: CRC Press [Google Scholar]
  35. Lauritzen SL, Dawid AP, Larsen BN, Leimer HG. 1990. Independence properties of directed Markov fields. Networks 20:491–505 [Google Scholar]
  36. Madigan D, Stang PE, Berlin JA, Schuemie M, Overhage M. et al. 2014. A systematic statistical approach to evaluating evidence from observational studies. Annu. Rev. Stat. Appl. 1:11–39 [Google Scholar]
  37. Manski CF. 1990. Nonparametric bounds on treatment effects. Am. Econ. Rev. Pap. Proc. 80:319–23 [Google Scholar]
  38. Martens EP, Pestman WR, de Boer A, Belitser SV, Klungel OH. 2006. Instrumental variables: applications and limitations. Epidemiology 17:260–67 [Google Scholar]
  39. Meek C, Glymour C. 1994. Conditioning and intervening. Br. J. Philos. Sci. 45:1001–21 [Google Scholar]
  40. Neyman J. 1935. Statistical problems in agricultural experimentation. J. R. Stat. Soc. Suppl. 2:107–54; discussion154–80 [Google Scholar]
  41. Pearl J. 1986. A constraint–propagation approach to probabilistic reasoning. Proc. First Conf. Uncertain. Artif. Intell. LN Kanal, JF Lemmer 357–70 Amsterdam: North-Holland [Google Scholar]
  42. Pearl J. 1988. Probabilistic Reasoning in Intelligent Systems San Mateo, CA: Morgan Kaufmann [Google Scholar]
  43. Pearl J. 2009. Causality: Models, Reasoning and Inference Cambridge, UK: Cambridge Univ. Press, 2nd ed.. [Google Scholar]
  44. Price H. 1991. Agency and probabilistic causality. Br. J. Philos. Sci. 42:157–76 [Google Scholar]
  45. Raiffa H. 1968. Decision Analysis Reading, MA: Addison-Wesley [Google Scholar]
  46. Robins JM. 1986. A new approach to causal inference in mortality studies with sustained exposure periods—application to control of the healthy worker survivor effect. Math. Model. 7:1393–512 [Google Scholar]
  47. Rosenbaum PR. 2010. Design of Observational Studies New York: Springer [Google Scholar]
  48. Rosenbaum PR, Rubin DB. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55 [Google Scholar]
  49. Rubin DB. 1978. Bayesian inference for causal effects: the role of randomization. Ann. Stat. 6:34–68 [Google Scholar]
  50. Shpitser I, Pearl J. 2006a. Identification of conditional interventional distributions. Proc. Twenty-Second Conf. Uncertain. Artif. Intell. R Dechter, T Richardson 437–44 Arlington, VA: AUAI Press [Google Scholar]
  51. Shpitser I, Pearl J. 2006b. Identification of joint interventional distributions in recursive semi-Markovian causal models. Proc. Twenty-First Conf. Uncertain. Artif. Intell. F Bacchus, T Jaakkola 1219–26 Arlington, VA: AUAI Press [Google Scholar]
  52. Spirtes P, Glymour C, Scheines R. 2000. Causation, Prediction and Search New York: Springer-Verlag, 2nd ed.. [Google Scholar]
  53. Spirtes P, Glymour C, Scheines R, Meek C, Fienberg S, Slate E. 1999. Prediction and experimental design with graphical causal models. See Glymour & Cooper 1999 65–93
  54. Verma T, Pearl J. 1990. Causal networks: semantics and expressiveness. Proc. Fourth Conf. Uncertain. Artif. Intell. RD Shachter, TS Levitt, LN Kanal, JF Lemmer 69–76 Amsterdam: North-Holland [Google Scholar]
  55. Verma T, Pearl J. 1991. Equivalence and synthesis of causal models. Proc. Sixth Conf. Uncertain. Artif. Intell., ed. PP Bonissone, M Henrion, LN Kanal, JF Lemmer 255–68 Amsterdam: North-Holland [Google Scholar]
  56. Wilk MB, Kempthorne O. 1955. Fixed, mixed and random models. J. Am. Stat. Assoc. 50:1144–67 [Google Scholar]
  57. Woodward J. 2003. Making Things Happen: A Theory of Causal Explanation Oxford, UK: Oxford Univ. Press [Google Scholar]
  58. Woodward J. 2013. Causation and manipulability. The Stanford Encyclopedia of Philosophy EN Zalta. http://plato.stanford.edu/archives/win2013/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-010814-020105
Loading
/content/journals/10.1146/annurev-statistics-010814-020105
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