Advertisement

Improving Institutions of Risk Management: Uncertain Causality and Judicial Review of Regulations

  • Louis Anthony Cox Jr.
  • Douglas A. Popken
  • Richard X. Sun
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 270)

Abstract

This chapter continues to consider questions of applied benefit-cost analysis and effective risk management, building on themes introduced in the previous two chapters. It expands the scope of the discussion to include a law-and-economics perspective on how different institutions—regulatory and judicial—involved in societal risk management can best work together to promote the public interest. In the interests of making the exposition relatively self-contained, we briefly recapitulate distinctions among types of causality and principles of causal inference that are discussed in more detail in Chap.  2, as well as principles of benefit-cost analysis and risk psychology, including heuristics and biases, from Chap.  10. In this chapter, however, the focus is less on individual, group, or organizational decision-making than on how rigorous judicial review of causal reasoning might improve regulatory risk assessment and policy.

References

  1. Aliferis CE, Statnikov A, Tsamardinos I, Mani S, Koutsoukos XS (2010) Local causal and Markov Blanket induction for causal discovery and feature selection for classification part I: algorithms and empirical evaluation. J Mach Learn Res 11:171–234Google Scholar
  2. Apte JS, Marshall JD, Cohen AJ, Brauer M (2015) Addressing global mortality from ambient PM2.5. Environ Sci Technol 49(13):8057–8066CrossRefGoogle Scholar
  3. Bareinboim E, Pearl J (2013) Causal transportability with limited experiments. In: Proceedings of the 27th AAAI conference on artificial intelligence. AAAI Press, Palo Alto, pp 95–101Google Scholar
  4. Bartholomew MJ, Vose DJ, Tollefson LR, Travis CC (2005) A linear model for managing the risk of antimicrobial resistance originating in food animals. Risk Anal 25(1):99–108CrossRefGoogle Scholar
  5. Bontempi G, Flauder M (2015) From dependency to causality: a machine learning approach. J Mach Learn Res 16:2437–2457. https://arxiv.org/abs/1412.6285 Google Scholar
  6. Clancy L, Goodman P, Sinclair H, Dockery DW (2002) Effect of air-pollution control on death rate in Dublin, Ireland: an intervention study. Lancet 360:1210–1214CrossRefGoogle Scholar
  7. Coglianese C (2001) Is consensus an appropriate basis for regulatory policy? In: Orts EW, Deketelaere K (eds) Environmental contracts: comparative approachesto regulatory innovation in the United States and Europe. Kluwer Law International, London, pp 93–114Google Scholar
  8. Cover TM, Thomas JA (2006) Elements of information theory, 2nd edn. Wiley, Hoboken, NJ. ISBN: 13 978-0-471-24195-9, ISBN: 10 0-471-24195-4. https://archive.org/details/ElementsOfInformationTheory2ndEd. Accessed 1 Nov 2018Google Scholar
  9. Cox LA Jr (2017) Do causal concentration-response functions exist? A critical review of associational and causal relations between fine particulate matter and mortality. Crit Rev Toxicol 47(7):603–631.  https://doi.org/10.1080/10408444.20 CrossRefGoogle Scholar
  10. Cox LA Jr, Popken DA (2015) Has reducing fine particulate matter and ozone caused reduced mortality rates in the United States? Ann Epidemiol 25(3):162–173CrossRefGoogle Scholar
  11. Cox LA Jr, Popken DA, Ricci PF (2013) Warmer is healthier: effects on mortality rates of changes in average fine particulate matter (PM2.5) concentrations and temperatures in 100 U.S. cities. Regul Toxicol Pharmacol 66:336–346CrossRefGoogle Scholar
  12. Cromar KR, Gladson LA, Perlmutt LD, Ghazipura M, Ewart GW (2016) American Thoracic Society and Marron Institute report. Estimated excess morbidity and mortality caused by Air Pollution above American Thoracic Society-Recommended Standards, 2011–2013. Ann Am Thorac Soc 13(8):1195–1201CrossRefGoogle Scholar
  13. Dawid PA (2008) Beware of the DAG! J Mach Learn Res 6:59–86. Workshop and conference proceedingsGoogle Scholar
  14. Dekker SWA, Woods DD (2009) The high reliability organization perspective. In: Human factors in aviation. 2nd edn., pp 123–143CrossRefGoogle Scholar
  15. Department of Housing, Planning, Community, and Local Government (2016) https://www.dccae.gov.ie/en-ie/environment/topics/air-quality/smoky-coal-ban/Pages/default.aspx
  16. Dockery DW, Rich DQ, Goodman PG, Clancy L, Ohman-Strickland P, George P, Kotlov T, HEI Health Review Committee (2013) Effect of air pollution control on mortality and hospital admissions in Ireland. Res Rep Health Eff Inst 176:3–109Google Scholar
  17. Dominici F, Greenstone M, Sunstein CR (2014) Science and regulation. Particulate matter matters. Science 344(6181):257–259CrossRefGoogle Scholar
  18. EPA (2011a) The benefits and costs of the clean air act from 1990 to 2020: summary report. U.S. EPA, Office of Air and Radiation, Washington, DC. www.epa.gov/air/sect812/aug10/summaryreport.pdf Google Scholar
  19. EPA (2011b) The benefits and costs of the clean air act from 1990 to 2020. Full report. U.S. EPA, Office of Air and Radiation, Washington, DC. http://www.epa.gov/oar/sect812/feb11/fullreport.pdf Google Scholar
  20. Fedak KM, Bernal A, Capshaw ZA, Gross S (2015) Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology. Emerg Themes Epidemiol 12:14CrossRefGoogle Scholar
  21. Frey L, Fisher D, Tsamardinos I, Aliferis CF, Statnikov A (2003) Identifying Markov Blankets with decision tree induction. In: Proceedings of the third IEEE international conference on data mining, Melbourne, FL, 19–22 November 2003. pp 59–66Google Scholar
  22. Friston K, Moran R, Seth AK (2013) Analysing connectivity with Granger causality and dynamic causal modelling. Curr Opin Neurobiol 23(2):172–178CrossRefGoogle Scholar
  23. Furqan MS, Siyal MY (2016) Random forest Granger causality for detection of effective brain connectivity using high-dimensional data. J Integr Neurosci 15(1):55–66CrossRefGoogle Scholar
  24. Gamble M (2013) 5 Traits of high reliability organizations: how to hardwire each in your organization. Becker’s Hospital Review, 29 Apr 2013. https://www.beckershospitalreview.com/hospital-management-administration/5-traits-of-high-reliability-organizations-how-to-hardwire-each-in-your-organization.html
  25. Gardner D (2009) The science of fear: how the culture of fear manipulates your brain. Penguin Group, New York, NYGoogle Scholar
  26. Gelman A, Zelizer A (2015) Evidence on the deleterious impact of sustained use of polynomial regression on causal inference. Res Polit:1–7. http://www.stat.columbia.edu/~gelman/research/published/rd_china_5.pdf
  27. Greenland S (2005) Multiple-bias modelling for analysis of observational data. J R Stat Soc A Stat Soc 168(Part 2):267–306CrossRefGoogle Scholar
  28. Halliday DM, Senik MH, Stevenson CW, Mason R (2016) Non-parametric directionality analysis—extension for removal of a single common predictor and application to time series. J Neurosci Methods 268:87–97CrossRefGoogle Scholar
  29. Hammond PJ (1992) Harsanyi’s utilitarian theorem: a simpler proof and some ethical connotations. In: Selten R (ed) Rational interaction: essays in honor of John Harsanyi. Springer, BerlinGoogle Scholar
  30. Harsanyi JC (1955) Cardinal welfare, individualistic ethics, and interpersonal comparisons of utility. J Polit Econ:309–321CrossRefGoogle Scholar
  31. Hendry DF (2004) Causality and exogeneity in non-stationary economic time-series. In: Welfe A (ed) Contributions to economic analysis, vol 269. Centre for Philosophy of Natural and Social Science, London, pp 21–48Google Scholar
  32. Hill AB (1965) The environment and disease: association or causation? Proc R Soc Med 58:295–300Google Scholar
  33. Hill J (2016) Atlantic causal inference conference competition: is your SATT where it’s at? http://jenniferhill7.wixsite.com/acic-2016/competition
  34. Höfler M (2005) The Bradford Hill considerations on causality: a counterfactual perspective. Emerg Themes Epidemiol 2:11CrossRefGoogle Scholar
  35. Hoover KD (2012) Causal structure and hierarchies of models. Stud Hist Phil Biol Biomed Sci 43(4):741–830.  https://doi.org/10.1016/j.shpsc.2012.05.007 CrossRefGoogle Scholar
  36. Hurd HS, Malladi S (2008) A stochastic assessment of the public health risks of the use of macrolide antibiotics in food animals. Risk Anal 28(3):695–710CrossRefGoogle Scholar
  37. Iwasaki Y (1988) Causal ordering in a mixed structure. In: Proceedings of the 27th AAAI conference on artificial intelligence. AAAI Press, Palo AltoGoogle Scholar
  38. Joffe M, Gambhir M, Chadeau-Hyam M, Vineis P (2012) Causal diagrams in systems epidemiology. Emerg Themes Epidemiol 9:1CrossRefGoogle Scholar
  39. Kahneman D (2011) Thinking, fast and slow. Farrar, Straus, and Giroux, New YorkGoogle Scholar
  40. Kelly O (2016) How the coal ban dealt with Dublin’s burning issue. The prohibition of ‘smoky’ coal in 1990 resulted in 350 fewer annual deaths in city. The Irish Times. www.irishtimes.com/news/environment/how-the-coal-ban-dealt-with-dublin-s-burning-issue-1.2367021. Accessed 26 Sept 2015
  41. Kleinberg S, Hripcsak G (2011) A review of causal inference for biomedical informatics. J Biomed Inform 44(6):1102–1112CrossRefGoogle Scholar
  42. Lee S, Honavar V (2013) Causal transportability of experiments on controllable subsets of variables: z-transportability. In: Proceedings of the 27th AAAI conference on artificial intelligence. AAAI Press, Palo AltoGoogle Scholar
  43. Lin H, Liu T, Fang F, Xiao J, Zeng W, Li X, Guo L, Tian L, Schootman M, Stamatakis KA, Qian Z, Ma W (2017) Mortality benefits of vigorous air quality improvement interventions during the periods of APEC Blue and Parade Blue in Beijing, China. Environ Pollut 220:222–227CrossRefGoogle Scholar
  44. Lucas RM, McMichael AJ (2005) Association or causation: evaluating links between “environment and disease”. Bull World Health Organ 83:792–795Google Scholar
  45. Nelson JM, Chiller TM, Powers JH, Angulo FJ (2007) Fluoroquinolone-resistant Campylobacter species and the withdrawal of fluoroquinolones from use in poultry: a public health success story. Clin Infect Dis 44(7):977–980CrossRefGoogle Scholar
  46. O’Malley AJ (2012) Instrumental variable specifications and assumptions for longitudinal analysis of mental health cost offsets. Health Serv Outcomes Res Methodol 12(4):254–272CrossRefGoogle Scholar
  47. Pearl J (2009) Causality: models, reasoning and inference, 2nd edn. Cambridge University Press, New York, NYCrossRefGoogle Scholar
  48. Pearl J (2010) An introduction to causal inference. Int J Biostat 6(2):7CrossRefGoogle Scholar
  49. Powell MR (2016) Trends in reported foodborne illness in the United States; 1996-2013. Risk Anal 36(8):1589–1598CrossRefGoogle Scholar
  50. Price LB, Lackey LG, Vailes R, Silbergeld E (2007) The persistence of fluoroquinolone-resistant Campylobacter in poultry production. Environ Health Perspect 115(7):1035–1039CrossRefGoogle Scholar
  51. Rottman BM, Hastie R (2014) Reasoning about causal relationships: inferences on causal networks. Psychol Bull 140(1):109–139.  https://doi.org/10.1037/a0031903 CrossRefGoogle Scholar
  52. Schoemaker PJH, Tetlock PE (2016) Superforecasting: how to upgrade your company’s judgment. Harv Bus Rev 94:72–78. https://hbr.org/2016/05/superforecasting-how-to-upgrade-your-companys-judgment
  53. Schwartz J, Austin E, Bind MA, Zanobetti A, Koutrakis P (2015) Estimating causal associations of fine particles with daily deaths in Boston. Am J Epidemiol 182(7):644–650CrossRefGoogle Scholar
  54. Simon HA (1953) Causal order and identifiability. In: Hood WC, Koopmans TC (eds) Studies in econometric method. Cowles Commission Monograph. Wiley, New York, pp 49–74Google Scholar
  55. Spiegelhalter DJ (1986) Computers, expert systems, and ADRs: can causality assessment be automated? Drug Inf J 20:543–550CrossRefGoogle Scholar
  56. Stokey NL (2008) The economics of inaction: stochastic control models with fixed costs. Princeton University, PrincetonCrossRefGoogle Scholar
  57. Tetlock PE, Gardner D (2015) Superforecasting: the art and science of prediction. Penguin Random House LLC, New York, NYGoogle Scholar
  58. Tian J, Pearl J (2000) Probabilities of causation: bounds and identification. Ann Math Artif Intell 28:287–313CrossRefGoogle Scholar
  59. Tikka S (2018) Package “causal effect”: deriving expressions of joint interventional distributions and transport formulas in causal models. The Comprehensive R Archive Network 1.3.6. https://cran.r-project.org/web/packages/causaleffect/index.html
  60. Thaler R (2015) Misbehaving: the making of behavioral economics. W. W. Norton and Company, New YorkGoogle Scholar
  61. Todd BS (1992) An introduction to expert systems. Oxford University, OxfordGoogle Scholar
  62. Voortman M, Dash D, Druzdzel MJ (2010) Learning causal models that make correct manipulation predictions with time series data. J Mach Learn Res 6:257–266Google Scholar
  63. Walker C (2016) Courts regulating the regulators. Oxford Business Law Blog, 1 May. https://www.law.ox.ac.uk/business-law-blog/blog/2016/05/courts-regulating-regulators
  64. Wang Y, Kloog I, Coull BA, Kosheleva A, Zanobetti A, Schwartz JD (2016) Estimating causal effects of long-term PM2.5 exposure on mortality in New Jersey. Environ Health Perspect 124(8):1182–1188CrossRefGoogle Scholar
  65. Weick KE, Sutcliffe KM (2001) Managing the unexpected—assuring high performance in an age of complexity. Jossey-Bass, San Francisco, CA, pp 10–17Google Scholar
  66. Wittmaack K (2007) The big ban on bituminous coal sales revisited: serious epidemics and pronounced trends feign excess mortality previously attributed to heavy black-smoke exposure. Inhal Toxicol 19:343–350CrossRefGoogle Scholar
  67. Woodward J (2013) Causation and manipulability. In: Zalta EN (ed) The stanford encyclopedia of philosophy. http://plato.stanford.edu/archives/win2013/entries/causation-mani/ Google Scholar
  68. Wu MH, Frye RE, Zouridakis G (2011) A comparison of multivariate causality based measures of effective connectivity. Comput Biol Med 41(12):1132–1141CrossRefGoogle Scholar
  69. Wynne B (1993) Public uptake of science: a case for institutional reflexivity. Public Underst Sci 2(4):321–337CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Louis Anthony Cox Jr.
    • 1
  • Douglas A. Popken
    • 2
  • Richard X. Sun
    • 3
  1. 1.Cox AssociatesDenverUSA
  2. 2.Cox AssociatesLittletonUSA
  3. 3.Cox AssociatesEast BrunswickUSA

Personalised recommendations