Part II has laid the heuristic foundations for reasoning with uncertain facts and models as a means-ends framework for judicial reasoning. The framework uses probabilistic measures and methods consistent with judicial and legislative reasoning. Although we have used probability and statistical theory as the means to achieve a just expression of causation, there are other, equally formal, methods to represent uncertainty and variability.
We have heuristically demonstrated that the management of causation in toxic tort law would greatly benefit from using the methods and principles that we have described and exemplified. To do otherwise invites chaotic and unjust allocations of liability.
A most recent case casts some light on the issues discussed in this paper. A critical aspect of Kumho, just decided by the US Supreme Court, as a measure of Daubert-reliability, is that the Court confirms for the third time in five years a "judicial broad latitude" as a means to allow scientific gatekeeping by trial judges.103Kumho holds that Daubert applies to all contexts to which FRE 702 applies,104 regardless of whether scientific, technical, or other specialized knowledge is being introduced into the controversy. The FRE 702 "latitude" allowed to experts' opinions is balanced by the Daubert, Joiner, and Kumho "latitude" given to trial judges to admit that evidence.
As discussed in Part I, Daubert stands for the admissibility of scientific, knowledge requiring expert testimony, under FRE 702, through four factors.'
The critical term is 'knowledge",' not the adjectives modifying it.107 Those four factors "may be" required to establish the reliability of expert knowledge. These are not the sole factors: the context of the case determines the appropriate number of factors to be set up by the trial judge.108 This reflects the inherent "flexibility" of the wording of FRE 702.109
The argument used by the US Supreme Court to ascertain whether or not expert evidence is admissible at trial is that:
it is not the "reasonableness" of the expert's use of a specific form of inference to determine the causation that is unreliable; but rather
it is the "reasonableness" of a specific form of inference and the expert's methods for analysis and deriving conclusion about the cause of failure (of tyre damage leading to a car crash that killed one person and injured three more) that is unreliable.10
In other words, if there are relevant but unaccounted initiating and causal events, and the causal analysis is purely judgmental, then the expert's testimony can be insufficient to pass through the Judicial gates. The process is inductive, or more precisely, empirico-inductive."
Kumho correctly extends Daubert beyond scientific knowledge and requires trial judges to establish a flexible protocol capable of filtering out dubious evidence. Reliable and admissible expert evidence must be cleared according to a coherent frame of reference for the parties and the judge. The "leeway" allowed by Kumho is precisely matched by the analytical aspects developed in Parts I and II of this work. The Kumho Court appears to believe that the Federal Rules of Evidence both expedite trials, and seek the "truth" and the "just determination" of the judicial process.12 The trilogy of Daubert, Joinder and Kumho still provides no operational or coherent framework for balancing the latitudes give to experts and to trial judges. We are now even more convinced than ever that our suggestions are worth further development.
† Part I of this article appeared in Volume 21(3) of the University of New South Wales Law Journal
* University of California, Berkeley, USA; PhD, LLM, MPA, MA, MSc. Ricci & Molton, 685 Hilldale Ave, Berkeley, CA 94708, USA; Professor, Faculty of Law, University of Wollongong, Australia.
** BSc, LLB (Hons) (Woll).
1 The injustice of requiring a deterministic description of the disease process which science has not yet been able to deliver is discussed by JB Brennan and A Carter, "Legal and Scientific Probability of Causation of Cancer and other Environmental Diseases in Individuals" (1985) 10 Journal of Health, Politics, Policy and Law 33.
2 B Holmstrom and R Meyerson, "Efficient and Durable Decision Rules with Incomplete Information" (1983) 51 Econometrica 1799.
3 See, generally, A Roth (ed), Came Theoretic Models of Bargaining, Cambridge University Press (1985).
4 Allen et al v US 588 F Supp 247 (1984); reversed on other grounds, 816 F 2d 1417 (10th Cir 1988); certiorari denied, 484 US 1004 (1988).
5Ibid at 416-17.
6Cottle v Superior Court 3 Cal App 4th 1367 (1992) at 1384-5.
7 See Green v American Tobacco Co 391 F 2d 97 (5th Cir 1968); rehearing (en banc), 409 F 2d 1166 (5th Cir 1969). Bowman v Twin Falls 581 P 2d 770 (Id 1978) at 774 held that: "to require certainty when causation itself is defined in terms of statistical probability is to ask for too much".
8 H Hams, "Toxic Tort litigation and the Cause Element: Is There Any Hope of Reconciliation?" (1986) 40 Southwestern Law Journal 909.
9 J von Plato, Creating Modern Probability, Cambridge University Press (1994) p 167.
10 Ibid, p 75 (footnote and emphasis omitted).
11 Ibid, p 76 (footnote omitted).
12 A Einstein, "Zum Gegenwartigen Stand des Strahlungsproblems" (1909) 10 Physikalische Zeitschrift 185.
13 Von Plato, note 9 supra, p 158.
14 Ibid, p 150.
15 H Reichembach, "Stetige Wahrscheinlichkeitsrechnung" (1929) 53 Zeitschrift far Physik 274.
16 Von Plato, note 9 supra, p 43.
17 H Weyl, Philosophie der Mathematik and Naturwissenschaft, Oldenbourg (1927).
18 JW Gibbs, Elementary Principles of Statistical Mechanics, Dover (1960) p 17.
19 R von Mises, Mathematical Theory of Probability and Statistics, Academic Press (1964).
20 Ibid, pp 183-97.
21 A Kolmogorov, "Logical Basis for Information Theory and Probability Theory" (1968) 14 Institute of Electrical and Electronic Engineers: Transactions of Information Technology 662.
22Von Plato, now 20 supra, p 205.
23Ibid, p 272.
24 !bid at 273-6.
25B de Finetti, "Fondamenti Logici del Ragionamento Probabilistico" (1930) 5 Bollettino Unione Matematica Italians 258 at 260.
26 A leading case establishing the common sense test is Bennett v Minister of Community Welfare (1992) 176 CLR 408 at 412, per Mason CJ, Deane and Toohey JJ: "In the realm of negligence, causation is essentially a question of fact, to be resolved as a matter of common sense. In resolving that questions, the `but for' test, applied as a negative criterion of causation, has an important role to play but is not a comprehensive and exclusive test of causation; value judgments and policy considerations necessarily intrude". This statement stems from the majority judgment in March v E & MH Stramare (1991) 171 CLR 506, and has since been approved in Steele v Twin City Rigging Pty Ltd (1993) 114 FLR 99 at 109, and Medlin v State Government Insurance Commission (1995) 182 CLR 1 at 6. An argument for probabilistic reasoning was, however, made by Murphy J in TNT Management Pty Ltd v Brooks (1979) 23 ALR 345, which was discussed in detail in Part I of this article.
27 (1938) 60 CLR 336 at 361 (emphasis added). This was followed in the mesothelioma case of Wintle v Conaust  VR 951 at 953. This statement may also be interpreted as raising the required standard of proof (on the balance of probabilities) above 51 per cent.
28 I Macduff, "Causation, Theory and Uncertainty" (1978) 9 Victoria University of Wellington Law Review 87 at 93.
29 E Adeney, "The Challenge of Medical Uncertainty: Factual Causation in Anglo-Australian Toxic Tort Litigation" (1993) 19 Monash University Law Review 23 at 24.
30 J Cohen, "The Value of Value Symbols in Law" in Smith and Weisstib (eds), The Western Idea of Law, Butterworths (1983) I at 8.
31 See Chapman v Hearse (1961) 106 CLR 112 at 122.
32 The California Supreme Court "disapproved" the use of "proximate cause" in favour of the "substantial factor". See Mitchell v Gonzales 54 Cal 3d 1041 (1991), cited in Pamela Lee v Heydon (1994) CDOS 4265 at 4265-6. In the Restatement of Torts, II § 431 `substantial' relates to the defendant's conduct to the extent it results in harm which "reasonable men ... regard ... as cause, using the word in the popular sense".
33 162 NE 99 (NY 1928).
34 Ibid at 103.
35 Ibid at 105.
36 Foreseeability concerns the determination of duty. See Roland v Christian 69 Cal 2d 108 (1968).
37 Thing v La Chusa 48 Cal 3d 644 (1989) at 668.
38 Law and economics theorists argue that legal causation should be replaced by `social efficiency' via a form of Judge Learned Hand's test, US v Carroll Towing 159 F 2d 169 (2nd Cir 1947). Because this test yields an expected value, probabilistic causation remains.
39 Adeney , note 29 supra at 59.
40 Hyatt v Sierra Boat Co 79 Cal App 3d 325 (1981) at 337-9, rejecting expert's testimony not being reasonably with the field of expertise; Pacific Gas and Electric Co v Zuckerman 189 Cal App 3d 1113 (1987) at 1135: "the value of opinion evidence rests not only in the conclusion reached but in the factors considered and reasoning employed". In accord: De Luca v Merrell Dow Pharmaceutical Inc 791 F Supp 1042 (DNJ 1992) at 1047.
EK Christie, "Toxic Tort Disputes: of of Causation and the Courts" (1992) 9 Environmental and PlanningLaw Journal 302 at 310; PF Ricci and LS Molton, "Risk and Benefits in Environmental Law" (1981) 214 Science 1096.
Daubers v Merrell Dow Pharmaceuticals Inc 43 F 3d 1311 (9th Cir 1995) at 1321, (citations omitted).
The problem is that the number of options may reach into the thousands.
US EPA, Risk Assessment Guidance for Superfund, Vol 1 Human Health Evaluation Manual (Part A), EPA/540/1-89/002 (December 1989).
US EPA, Supplemental Guidance to RAGS: Calculating the Concentration Term, PB92 - 963373 (May 1992) at 2.
!bid at 3.
US EPA, Guidelines for Exposure Assessment, 57 FR 22888-938 (1992).
49 The actual definition of a `stage' in cancer process is difficult, "[a] rough general rule is that if a change is not likely to have happened within 10 years of a cell being ready for it, then it would count as a stage, but if it is likely to take less than a year it would not": M Kiah, JD Watson and H Winsten (eds), Origins of Human Cancer, vol 4 Cold Spring Harbor Laboratory NY (1977) p 1403.
50 One way for overcoming this problem is to use physiologically based pharmaco-kinetic (PB-PK) models. These yield the concentrations of the ultimate by-products of biochemical reactions, from the original chemical, to the target tissue, cell or DNA. This is the dose, often measured in milligrams per kilogram of body weight per day.
51 R Jeffrey, Probability and the An of Judgement, Cambridge University Press (1992). See, in particular: ch 2, p 15.
52!bid, p 14 (footnote omitted). Jeffrey cites Rudner's view that: "for, since no scientific hypothesis is ever completely verified, in accepting a hypothesis the scientist must make the decision that the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis ... [which] is going to be a function of the importance, in the typical ethical sense, of making a mistake in accepting or rejecting the hypothesis" (emphasis in original).
53 The probability value is the proportion of events, out of the total number of events, which do not support the null hypothesis of no effect.
54 Office of Technology Assessment, Assessment of Technologies for Determining Cancer Risks in the Environment, 1981; TJ Gill, GJ Smith, RW Wissler and HW Kunz, "The Rat as an Experimental Animal" (1989) 245 Science 269.
55 Gill et al, note 54 supra at 272. See also S Reynolds, S Stowers, R Patterson, R Maronpot, S Aaronson and M Anderson, "Activated Oncogenes in B6C3F1 Mouse Liver Tumors: Implications for Risk Assessment" (1987) 237 Science 1309 at 1310.
56 The data are cited in the US EPA data base IRIS, 11 February 1994.
57 The "weights" are: (A) human carcinogen; (B1) probable human carcinogen from "limited" human data; (B2) probable human carcinogen with sufficient evidence in animals and inadequate or no evidence in humans; (C) possible human carcinogen; (D) not classifiable as to human carcinogenicity; and (E) evidence of noncarcinogenicity for humans. See, Risk Assessment Guidance for Superfund, Part A, EPA/540/l-89/002 at 7-11.
61 See, for discussion, JM Bishop, "Oncogenes and Clinical Cancer" in RA Weinberg (ed), Oncogenes and the Molecular Origins of Cancer, Cold Spring Press (1989) 327.
62 Note 58 supra at 7.
63 Ibid at 3.
64 Ibid at 4.
65 National Research Council, Science and Judgment in Risk Assessment, National Academy Press, Washington DC (1994) at H-2-5, Table H-3.
66 MA Pereira and LW Chang, "Binding of Chloroform to Mouse and Rat Hemoglobin" (1982) 39 Chemico-Biological Interactions 89.
67 E Bayley, T Connors, P Farmer, S Gorf, and S Rickard, "Methylation of Cysteine in Hemoglobin Following Exposure to Methylating Agents" (1981) 41 Cancer Research 2514.
68 IC Hsu, MC Poirier, SH Yuspa, RH Yolken, and CC Hams, "Ultrasensitive Enzymatic Radioimmunoassay (USERIA) Detects Femtomoles of Acetylaminofluorine-DNA Adducts" (1980) 1 Carcinogenesis 455.
69 E Culotta and DE Koshland, "The Molecule of the Year: How DNA Repair Works its Way to the Top" (1994) 266 Science 1926 at 1927.
70 DE Koshland, "Molecule of the Year: DNA Repair Enzymes" (1994) 266 Science 1925 at 1925.
72 Comprehensive Environmental Response, Compensation, and Liability Act 1980, (CERCLA/Superfund), 42 USC § 9601 et seq.
73 International Agency for Research on Cancer, Polynuclear Aromatic Compounds, Pan 1, Chemical, Environmental, and Experimental Data, vol 32 (1983); US EPA, Health Effects Assessment for Polycyclic Aromatic Hydrocarbons (PAH) ECAO, EPA 540/1-86-013 (1986).
74 The meaning of the symbols is as follows: means 'results in', ↓ means 'results in a repair' and OR means 'an alternative' (either a repaired DNA adduct or a mutation which goes unrepaired, but not both).
75 US Environmental Protection Agency, Risk Assessment Guidance for Superfund: Volume 1 - Human Health Evaluation Manual, Part C, (Risk Evaluation of Remedial Alternatives) Interim, Publication 9285.7 01C (December 1991), Office of Emergency and Remedial Response, Washington DC.
76 The duration of exposure is lifetime. The form of the LMS is Pr(d) = 1 - exp[-(q0 + q1d+ q2d2 + ... + qndn)]. Pr(.)is the lifetime probability of cancer, d is thelifetime dose, and qi are the parameters of the model estimated from experimental data. See KS Crump, "An Improved Procedure for Low-Dose Carcinogenic Risk Assessment from Animal Data" (1984) 5 Journal of Environmental Pathology and Toxicology 339.
77 Following LA Cox, "Assessing Cancer Risks: from Statistical to Biological Models" (1990) 116 Journal of Energy Engineering 189 at 199: "Explanation of carcinogenesis is organized around a few key parameters ... [which] provide the basic input data for the model, from which cancer hard rates are predicted .... Many of the key parameters [or surrogates for them] can potentially be measured directly in the laboratory [or in cellular systems], rather than being estimated statistically from whole-animal bioassay response data. Thus, the MVK model can potentially use the empirical data from molecular epidemiology".
78 Following SH Moolgavkar, "Cazcinogenesis Modeling: From Molecular Biology to Epidemiology" (1981) 7 Annual Review of Public Health 151, a simple formulation of this model yields the age-specific incidence rate for the cancer, given: the initial population of normal cells, the rates of cell transformation from the normal stage (containing stem cells) to the initiated stage (containing initiated cells) and from the initiated stage to the malignant stage (containing malignant cells), the average rates of cell formation (birth) and the average rates of cell death or differentiation.
79 Risk Assessment Guidance for Superfund, Human Health Evaluation Manual, vol 1, EPA/540/1-89/002 (December 1989) at 7-2.
80 R Doll and R Peto, "The Causes of Cancer: Quantitative estimates of Avoidable Risk of Cancer in the United States Today" (1981) 66 Journal of the National Cancer Institute 1195 at 1219.
81 Note 58 supra at 15. The criteria are: temporal relationship, consistency, magnitude of the association, biological gradient, specificity of the association, biological plausibility, and coherence.
82 Jeffrey, note 51 supra, p 3.
83 C Glymour, Theory and Evidence, Princeton University Press (1980) p 69.
84 Note 51 supra, pp 2-3.
85 Ibid, p 83.
86 Ibid p100.
87 D Clayton and M Hills, Statistical Models in Epidemiology, Oxford Science Publications (1994).
88 We let y be a vector of uncertain quantities to be predicted (eg, health responses), x be a matrix of explanatory variables (eg, one or more forms of exposures, socio-economic and other independent variables), and p(y; x, b) = pr(Y = y I X = x; b) be a prior conditional probability model for the relation between the two random variables X, Y, and b the vector of parameters that needs to be estimated. Given a probability model symbolised by pr(y; x, b), the likelihood function for b is pr(y; x, b) considered as a function of b instead of as a function of x and y. Then L(b; x, y, pr) denotes the likelihood function for b based on observed data vectors, for a probability model pr(.).
89 LA Cox and PF Ricci, "Dealing with Uncertainty: From Health Risks Assessment to Environmental Decisionmaking" (1992) 118 Journal of Energy Engineering 77.
A demonstration of Bayes' theorem follows. From pr(A and B) = pr(A)pr(BIA) and pr(B and A) = pr(B)pr(BIA) equate the right hand sides to obtain pr(AIB) = [pr(BIA)pr(A)]/pr(B). See DV Lindley, Bayesian Statistics: A Review, SIAM (1984).
J Pearl, "Bayesian and Belief-Functions Formalisms for Evidential Reasoning: A Conceptual Analysis" Proceedings of the 5th Israeli Symposium on Artificial Intelligence, December 1988.
The coherence means that the axiomatic properties of the system are established first, and the methods follow.
Lindley, note 90 supra, pp 72-3.
Pearl, note 91 supra.
AP Dempster, "A Generalization of Bayesian Inference (with discussion)" (1968) 30 Journal of the Royal Statistical Society (B) 205; G Shafer, "Belief Functions and Parametric models (with discussion)" (1982) 44 Journal of the Royal Statistical Society (B) 322.
96WG Cochran, "Problems Arising in the Analysis of a Series of Similar Experiments" (1937) 1 Journal of the Royal Statistical Society (Supplement 4) 102; L Hedges and I O1kin, Statistical Methods for Meta-Analysis, Academic Press (1985).
97R Fisher, "Combining Independent Tests of Significance"(1948) 2 The American Statistician 30.
98Peer review of heterogeneous material is assumed to be unbiased and to be able to separate the wheat from the chaff.
99This summarises the ideas by GV Glass, "Synthesizing Empirical Research: Meta-Analysis" in SA Ward and LI Reed (eds), Knowledge Structure and Use: Implication for Synthesis and Interpretation, Temple University Press (1983) 24.
100 916 F 2d 829 (3d Cir 1990) at 29.
101 WC Salmon, The Foundations of Scientific Inference, University of Pittsburgh Press (1966) p 18.
102 R Carnap, The Continuum of Inductive Methods, University of Chicago Press (1952)
103 Kumho Tire Co Ltd v Carmichael  US Lexis 2189 at 2195, US Supreme Court, decided 23 March 1999.
104 FRE 702 states that: "If scientific, technical, or other specialized knowledge will assist the trier of fact to understand the evidence or to determine a fact in issue, a witness qualified as an expert by knowledge, skill, experience, training, or education, may testify thereto in the form of an opinion or otherwise".
105 Daubert v Merrell Dow Pharmaceuticals Inc 509 US 579 (1993) at 592-4. The factors are whether (a) a "theory or techniques ... can be (and has been) tested"; (b) its "peer review and publication"; (c) its "known or potential rate of error" and whether there are "standards controlling the technique's operation"; and (d) its "general acceptance in the relevant scientific community". The factors "may bear on the judge's gatekeeping determination" and are not exhaustive: Kumho, note 103 supra at 2194.
106 Kumho, note 103 supra at 2193, citing Daubert. Daubert dealt with scientific knowledge, not with the other forms of evidence listed in the FRE 702.
107 Ibid, citing Daubert, note 105 supra at 589-90.
108 Ibid at 2195.
111 Ibid at 2193, citing Learned Hand's view that experts' "general truths are derived from ... specialized knowledge" (citation omitted).