In the 19th and early 20th century, scientists and lay people usually conceptualized causation as “deterministic.” Their model of science was perhaps what was called Newtonian, in which observations were invariably described in terms of identifiable forces that acted upon antecedent phenomena. The universe was akin to a pool table, with the movement of the billiard balls fully explained by their previous positions, mass, and movements. There was little need for probability to describe events or outcomes in such a universe.
The 20th century ushered in probabilistic concepts and models in physics and biology. Because tort law is so focused on claims of bodily integrity and harms, I am focused here on claimed health effects. Departing from the Koch-Henle postulates and our understanding of pathogen-based diseases, the latter half of the 20th century saw the rise of observational epidemiology and scientific conclusions about stochastic processes and effects that could best be understood in terms of probabilities, with statistical inferences from samples of populations. The language of deterministic physics failed to do justice to epidemiologic evidence or conclusions. Modern medicine and biology invoked notions of base rates for chronic diseases, which rates might be modified by environmental exposures.
In the wake of the emerging science of epidemiology, the law experienced a new horizon on which many claimed tortogens did not involve exposures uniquely tied to the harms alleged. Rather, the harms asserted were often diseases of ordinary life, but with that suggested the harms were quantitatively more prevalent or incident among people exposed to the alleged tortogen. Of course, the backwaters of tort law saw reactionary world views on trial, as with claims of trauma-induced cancer cases, which are with us still. Nonetheless, slowly but not always steadily, the law came to grips with probability and statistical evidence.
In law, as in science, a key component of causal attribution is counterfactual analysis. If A causes B, then if in the same world, ceteris paribus, we do not have A, then we don’t have B. Counterfactual analysis applies as much to stochastic processes that are causally influenced by rate changes, as they apply to the Newtonian world of billiard balls. Some writers in the legal academy, however, would opportunistically use the advent of probabilistic analyses of health effects to dispose of science altogether. No one has more explicitly exploited the opportunity than Professor Alexandra Lahav.
In an essay published in 2022, Professor Lahav advanced extraordinary claims about probabilistic causation, or what she called “chancy causation.”[1] The proffered definition of chancy causation is bumfuzzling. Lahav provides an example of an herbicide that is “associated” with the type of cancer that the heavily exposed plaintiff developed. She tells us that:
“[t]here is a chance that the exposure caused his cancer, and a chance that it did not. Probability follows certain rules, or tendencies, but these regular laws do not abolish chance. This is a common problem in modern life, where much of what we know about medicines, interventions, and the chemicals to which we are exposed is probabilistic. Following the philosophical literature, I call this phenomenon chancy causation.”[2]
So the rules of probability do not abolish chance? It is hard to know what Lahav is trying to say here. Probability quantifies chance, and gives us an understanding of phenomena and their predictability. When we can model an empirical process with a probability distribution, such as one that is independent and identically distributed, we can often make and test quantitative inferences about the anticipated phenomena.
Lahav vaguely acknowledges that her term, “chancy causation” is borrowed, but she does not give credit to the many authors who have used it before.[3] Lahav does note that the concept of probabilistic causation used in modern-day risk factor epidemiology is different from the deterministic causal claims that dominated tort law in the 19th and the first half of the 20th century. Lahav claims that chancy causation is inconsistent with counterfactual analysis, but she cites no support for her claim, which is demonstrably false. If we previously saw the counterfactual of if A then B, as key to causality, we can readily restate the counterfactual as a probability: A probably causes B. On a counterfactual analysis, then if we do not have A as an antecedent, then we probably do not have B. For a classic tortogen such as tobacco smoking, we can say confidently that tobacco smoking probably causes lung cancer. And for a given instance of lung cancer, we can say based upon the entire evidentiary display, that if a person did not smoke tobacco, he would probably not have developed lung cancer. Of course, the correspondence is not 100 percent, which is only to say that it is probabilistic. There are highly penetrant genetic mutations that may be the cause of a given lung cancer case. We know, however, that such mutations do not cause or explain the large majority of lung cancer cases.
Contrary to Lahav’s ipse dixits, tort law can incorporate, and has accommodated, both general and specific causation in terms of probabilistic counterfactuals. The modification requires us, of course, to address the baseline situation as a rate or frequency of events, and the post-exposure world as one with a modified rate or frequency. Without confusion or embarrassment, we can say that the exposure is the cause of the change in event rates. Modern physics similarly addresses whether we must be content with probability statements, rather than precise deterministic “billiard ball” physics, which is so useful in a game of snooker, but less so in describing the position of sub-atomic particles. In the first half of the 20th century, the biological sciences learned with some difficulty that it must embrace probabilistic models, in genetic science, as well as in epidemiology. Many biological causation models are completely stated in terms of probabilities that are modified by specified conditions.
Lahav intends for her rejection of counterfactual causality to do a lot of work in her post-modern program. By falsely claiming that chancy causation has no factual basis, Lahav jumps to the conclusion that what the law calls for is nothing but “policy,”[4] and “normative decision.”[5] Having fabricated the demise of but-for causation in the context of probabilistic relationships, Lahav suggests that tort law can pretend that the causation question is nothing more than a normative analysis of the defendant’s conduct. (Perhaps it is more than a tad revealing that she does not see that the plaintiff’s conduct is involved in the normative judgment.) Of course, tort law already has ample room for policy and normative considerations built into the concepts of duty and breach of duty.
As we saw with the lung cancer example above, the claim that tobacco smoking probably caused the smoker to develop lung cancer can be entirely factual, and supported by a probabilistic judgment. Lahav calls her erroneous move “pragmatic,” although it has no relationship to the philosophical pragmatism of Peirce or Quine. Lahav’s move is an incorrect misrepresentation of probability and of epidemiologic science in the name of compensation free-for-alls. Obtaining a heads in the flip of a fair coin has a probability of 50%; that is a fact, not a normative decision, even though it is, to use Lahav’s vocabulary, “chancy.”
Lahav’s argument is not always easy to follow. In one place, she uses “chancy” to refer to the posterior probability of the correctness of the causal claim:
“the counterfactual standard can be successfully defended against by the introduction of chance. The more conflicting studies, the “more chancy” the causation. By that I do not mean proving a lower probability (although this is a good result from a defense point of view) but rather that more, different study results create the impression of irreducible chanciness, which in turn dictates that the causal relation cannot be definitively proven.”[6]
This usage, which clearly refers to the posterior probability of a claim, is not necessarily limited to so-called non-deterministic phenomena. People could refer to any conclusion, based upon conflicting evidence of deterministic phenomena, as “chancy.”
Lurking in her essay is a further confusion between the posterior probability we might assign to a claim, or to an inference from probabilistic evidence, and the probability of random error. In an interview conducted by Felipe Jiménez,[7] Lahav was more transparent in her confusion, and she explicitly commited the transpositional fallacy with her suggestion that customary statistical standards (statistical significance) ensure that even small increased risks, say of 30%, are known to a high degree of certainty.
Despite these confusions, it seems fairly clear that Lahav is concerned with stochastic causal processes, and most of her examples evidence that concern. Lahav poses a hypothetical in which epidemiologic studies show smokers have a 20% increased risk of developing lung cancer compared with non-smokers.[8] Given that typical smoking histories convey relative risks of 20 to 30, or increased risks of 2,000 to 3,000%, Lahav’s hypothetical may readers think she is shilling for tobacco compaies. In any event, in the face of a 20% increased risk (or relative rsk of 1.2), Lahav acknowledges that the probability of a smoker’s developing lung cancer is higher than that of a non-smoker, but “in any particular case the question whether a patient’s lung cancer was caused by smoking is uncertain.” This assertion, however, is untrue; the question is not “uncertain.” She has provided a certain quantification of the increased risk. Furthermore, her hypothetical gives us a good deal of information on which we can say that smoking probably did not result in the patient’s lung cancer. Causation may be chancy because it is based upon a probabilistic inference, but the chances are actual known, and they are low.
Lahav posits a more interesting hypothetical when she considers a case in which there is an 80% chance that a person’s lung cancer is attributable to smoking.[9] We can understand this hypothetical better if we reframe it as classic urn probability problem. In a given (large) population of non-smokers, we expect 100 lung cancers per year. In a population of smokers, otherwise just like the population of non-smokers, we observe 500 lung cancers. Of the observed number, 100 were “expected” because they happen without exposure to the putative causal agent, and 400 are “excess.”The relative risk would be 5, or 400% increased risk, and still well below the actual measure of risk from long-term smoking, but the attributable risk would be [(RR-1)/RR] or 0.8 (or 80%). If we imagine an urn with 100 white “expected” balls, and 400 red “excess” balls added, then any given draw from the urn, with replacement, yields an 80% probability of a red ball, or an excess case. Of course, if we can see the color, we may come to a consensus judgment that the ball is actually red. But on our analogy to discerning the cause of a given lung cancer, we have at present nothing by way of evidence with which to call the question, and so it remains “chancy” or probabilistic. The question is not, however, in any way normative. The answer is different quantitatively in the 20% and in the 400% hypotheticals.
Lahav asserts that we are in a state of complete ignorance once a smoker has lung cancer.[10] This is not, however, true. We have the basis for a probabilistic judgment that will probably be true. It may well be true that the probability of attribution will be affected by the probability that the relative risk = 5 is correct. If the posterior probability for the claim that smoking causes lung cancer by increasing its risk 400% is only 30%, then of course, we could not make the attribution in a given case with an 80% probability of correctness. In actual litigation, the argument is often framed on an assumption arguendo that the increased risk is greater than two, so that only the probability of attribution is involved. If the posterior probability of the claim that exposure to the tortogen increased risk by 400% or 20,000% was only 0.49, then the plaintiff would lose. If the posterior probability of the increased risk was greater than 0.5, the finder of fact could find that the specific causation claim had been carried if the magnitude of the relative risk, and the attributable risk, were sufficiently large. This inference on specific causation would not be a normative judgment; it would be guided by factual evidence about the magnitude of the relevant increased risk.
Lahav advances a perverse skepticism that any inferences about individuals can be drawn from information about rates or frequencies in groups of similar individuals. Yes, there may always be some debate about what is “similar,” but successive studies may well draw the net tighter around what is the appropriate class. Lahav’s skepticism and her outright denialism about inferences from general causation to specific causation, are common among some in the legal academy, but it ignores that group to individual inferences are drawn in epidemiology in multiple contexts. Regressions for disease prediction are based upon individual data within groups, and the regression equations are then applied to future individuals to help predict those individuals’ probability of future disease (such as heart attack or breast cancer), or their probability of cancer-free survival after a specific therapy. Group to individual inferences are, of course, also the basis for prescribing decisions in clinical medicine. These are not normative inferences; they are based upon evidence-based causal thinking about probabilistic inferences.
In the early tobacco litigation, defendants denied that tobacco smoking caused lung cancer, but they argued that even if it did, and the relative risk were 20, then the specific causation inference in this case was still insecure because the epidemiologic study tells us nothing about the particular case. Lahav seems to be channeling the tobacco-company argument, which has long since been rejected on the substantive law of causation. Indeed, as noted, epidemiologists do draw inferences about individual cases from population-based studies when they invoke clinical prediction models such as the Framingham cardiovascular risk event model, or the Gale breast cancer prediction model. Physicians base important clinical interventions, both pharmacologic and surgical, for individuals upon population studies. Lahav asserts, without evidence, that the only difference between an intervention based upon an 80% or a 30% probability is a “normative implication.”[11] The difference is starkly factual, not normative, and describes a long-term likelihood of success, as well as an individual probability of success.
Post-Modern Causation
What we have in Lahav’s essay is the ultimate post-modern program, which asserts, without evidence, that when causation is “chancy,” or indeterminate, courts leave the realm of science and step into the twilight zone of “normative decisions.” Lahav suggests that there is an extreme plasticity to the very concept of causation such that causation can be whatever judges want it to be. I for one sincerely doubt it. And if judges make up some Lahav-inspired concept of normative causation, the scientific community would rightfully scoff.
Establishing causation can be difficult, and many so-called mass tort litigations have failed for want of sufficient, valid evidence supporting causal claims. The late Professor Margaret Berger reacted to this difficulty in a more forthright way by arguing for the abandonment of general causation, or cause-in-fact, as an element of tort claims under the law.[12] Berger’s antipathy to requiring causation manifested in her hostility to judicial gatekeeping of the validity of expert witness opinions. Her animus against requiring causation and gatekeeping under Rule 702 was so strong that it exceeded her lifespan. Berger’s chapter in the third edition of the Reference Manual on Scientific Evidence, which came out almost one year after her death, embraced the First Circuit’s notorious anti-Daubert decision in Milward, which also post-dated her passing.[13]
Professor Lahav has previously expressed a distain for the causation requirement in tort law. In an earlier paper, “The Knowledge Remedy,” Lahav argued for an extreme, radical precautionary principle approach to causation.[14] Lahav believes that the likes of David Michaels have “demonstrated” that manufactured uncertainty is a genuine problem, but not one that affects her main claims. Remarkably, Lahav sees no problem with manufactured certainty in the advocacy science of many authors or the lawsuit industry.[15] In “Chancy Causation,” Lahav thus credulously repeats Michaels’ arguments, and goes so far as to describe Rule 702 challenges to causal claims as having the “negative effect” of producing “incentives to sow doubt about epidemiologic studies using methodological battles, a strategy pioneered by the tobacco companies … .”[16] Lahav’s agenda is revealed by the absence of any corresponding concern about the negative effect of producing incentives to overstate the findings, or the validity of inferences, in order to obtain an unwarranted and unsafe verdicts for claimants.
[1] Alexandra D. Lahav, “Chancy Causation in Tort,” 15 J. Tort L. 109 (2022) [hereafter Chancy Causation].
[2] Chancy Causation at 110.
[3] See, e.g., David K. Lewis, Philosophical Papers: Volume 2 175 (1986); Mark Parascandola, “Evidence and Association: Epistemic Confusion in Toxic Tort Law,” 63 Phil. Sci. S168 (1996).
[4] Chancy Causation at 109.
[5] Chancy Causation at 110-11.
[6] Chancy Causation at 129.
[7] Felipe Jiménez, “Alexandra Lahav on Chancy Causation in Tort,” The Private Law Podcast (Mar. 29, 2021).
[8] Chancy Causation at 115.
[9] Chancy Causation at 116-17.
[10] Chancy Causation at 117.
[11] Chancy Causation at 119.
[12] Margaret A. Berger, “Eliminating General Causation: Notes towards a New Theory of Justice and Toxic Torts,” 97 Colum. L. Rev. 2117 (1997).
[13] Milward v. Acuity Specialty Products Group, Inc., 639 F.3d 11 (1st Cir. 2011), cert. denied sub nom., U.S. Steel Corp. v. Milward, 132 S. Ct. 1002 (2012).
[14] Alexandra D. Lahav, “The Knowledge Remedy,” 98 Texas L. Rev. 1361 (2020). See “The Knowledge Remedy Proposal” Tortini (Nov. 14, 2020).
[15] Chancy Causation at 118 (citing plaintiffs’ expert witness David Michaels, The Triumph of Doubt: Dark Money and the Science of Deception (2020), among others).
[16] Chancy Causation at 129.
