J ClinEpidemiol Vol. 45,No. 6, pp. 639-649,1992 Printed in Great Britain. All rights reserved

CAUSAL

PROPOSITIONS

0895-4356/92 $5.00 + 0.00 Copyright 6 1992 Pergamon Press Ltd

IN CLINICAL PRACTICE

MICHAEL S. KRAMER”*‘*

Departments of ‘Epidemiology and Biostatistics Medicine, Montreal, Quebec and 3Department

RESEARCH

AND

and DAVID A. LANES and ZPediatrics,

McGill

University

Faculty

of

of Theoretical Statistics, School of Statistics, University of Minnesota, Minneapolis, MN, U.S.A. (Received in recised form

2 January

1992)

Abstract-The concept of causation is central to clinical research and practice. The health science literature on causality, largely contributed by epidemiologists, has examined the population-based question of whether an exposure can cause a given health outcome. Most of this literature has focused on criteria for assessing causality, rather than attempting to define it. Moreover, the population-based approach is rather distant from the individual persons in whom causes must act, which has led to different

perspectives on causality among epidemiologists and health policy makers, on the one hand, and clinical practitioners and the lay public, on the other. We attempt to bridge the gap between these perspectives by defining three probabilistic causal propositions based on the locus (individual vs population) and time frame (past vs future outcome) to which they refer, beginning with the individual in whom a health outcome has already occurred (“retrodictive” causal propositions, i.e. It Did) and proceeding to “potential” causal propositions (Zt Can) for populations and “predictive” causal propositions (It Will) for individuals or populations. We conclude by showing how attention to these distinctions may help avoid common pitfalls that can impair clinical or public health decision-making.

Cause

Causality

Decision-making

Probability

INTRODUCTION

Adverse

drug

reactions

the morbidity and mortality “caused” by hypertension. But for all its importance, cause is not an easy concept to define or understand. For example, does the statement “Smoking causes lung cancer” mean (1) that all smokers will contract lung cancer (in the absence of competing risks), (2) that all lung cancer is caused by smoking, or (3) that there exists at least one individual whose lung cancer was or will be caused by smoking? The first two of these interpretations correspond, respectively, to the notions of sufficient and necessary causes; smoking is a suflcient cause of lung cancer if all smokers contract lung cancer, and it is a necessary cause if all lung cancer occurs among smokers. Although the idea that a “proper” cause must be both

Causation is of cardinal importance throughout health research, practice and policy. Considerable intellectual energy and money are expended in assessing whether this chemical, that virus, or some behavior “causes” a particular health outcome-and significant public policies can hinge on the result. Drugs that “cause” hepatitis or chemicals that “cause” cancer may be restricted or even banned. A medication or activity that “caused” a reduction in blood pressure may enjoy widespread use among hypertensives, who hope to lessen their risk of *All correspondence should be addressed to: Michael S. Kramer, 1020 Pine Avenue West, Montreal, Quebec, Canada H3A 1A2. 639

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necessary and sufficient underlies the famous Koch postulates of causality, it is now well recognized that neither of these criteria are met frequently enough to provide a useful foundation for the concept of causation in medicine. [Some causes are “necessary” tautologically, however, since certain diseases (outcomes) are defined to exist only if a particular microorganism (the necessary cause) can be isolated from the patient’s body. The idea that these microorganisms are also su@kxt causes was abandoned long ago; Koch himself recognized that not all exposed individuals contracted the disease in question [l].] Difficulties also arise in applying causal concepts to individual cases, or in distinguishing individual from generalizable, population-based notions. For example, Alice takes drug D for relief of joint pain and inflammation, but goes into anaphylactic shock. Why do we claim that “D caused Alice’s anaphylaxis” rather, say, than “Alice’s immune hypersensitivity caused it”? And can we ever know “for sure” that drug D “causes” anaphylaxis? Or that it caused Alice’s anaphylaxis? (What would it even mean to know the answers to these last two questions “for sure”? Would it mean the same thing for both questions?) There are two different kinds of problems here. The first is what it means to say that some exposure A causes (or caused) some outcome B to occur. The second is causality assessment: how we evaluate whether A in fact does (or did) cause B to occur. Philosophers have, of course, puzzled over both these problems for many centuries, but the results of their reflections have not been incorporated into the intellectual armamentarium of most clinicians and health scientists. In the past several decades, however, as their focus shifted from infectious to chronic diseases, epidemiologists have concerned themselves with the second problem, causality assessment, by developing criteria for evaluating epidemiologic evidence [224]. Sparked by the recent “rediscovery” of the writings of Karl Popper, epidemiologists have debated the roles of deductive vs inductive reasoning in making causal inferences [5-91. But with few exceptions [lO--131, such debate has included little discussion about the first problem, i.e. the meaning of cause. In this paper, we discuss the form and meaning of three clinically pertinent kinds of propositions that assert a causal connection between an exposure A and, an outcome B.

We then examine the implications for causality assessment that derive from the meanings we attach to these propositions, emphasizing in particular the role that probability should play in evaluating whether or not a given causal proposition is true. Finally, we point out some adverse consequences that can arise if the issues we raise are “swept under the rug” in epidemiologic research and clinical and public health decision-making. THE FORM AND MEANING PROPOSITIONS

OF CAUSAL

Locus and time frame

Unfortunately, causal assertions are often expressed in a vague shorthand that ignores where (the locus) and when (the time frame) the cause is acting. For example, the assertion “Smoking causes lung cancer” seems to posit an abstract, timeless relationship between two entities, but in fact it must refer to a specific set of events taking place in the past, present, or future in the bronchial or alveolar cells of one or more individuals. Perspectives on the locus of cause can differ profoundly. Epidemiologists and public health policy makers conceptualize cause at the locus of populations. According to this perspective, causal relationships are important because they can lead to public health interventions to prevent disease and promote health in the community. Clinicians, on the other hand, typically think about cause at the individual locus because the problems they face arise in individual patients who seek their care. Thus diagnosis involves an inference about the cause of a particular patient’s symptoms and signs; prognosis, an inference about a disease as cause of the patient’s future morbidity or mortality; and treatment, an inference about how a clinical intervention may cause a change in that prognosis. Lay persons too tend to view causal notions in terms of how a potential causal agent or manoeuvre affects them. Since causes of health and disease outcomes must act within individual, intact human beings, it may be helpful to define cause at the locus of the individual. (In fact, the events take place at a far more basic level: cell biology, molecular genetics and biochemistry, or even subatomic physics.) More importantly, we believe that an individual-based definition of cause can help bridge the gap in perspective among epidemiologists and public health policy

Causal

Propositions

makers, on the one hand, and clinicians and the lay public, on the other. Retrodictive causal propositions

One basic type of causal proposition refers to an individual locus and a past outcome: “Smoking caused John’s lung cancer.” Since such propositions start from the fact that a particular outcome has occurred (John’s lung cancer), we call them retrodictive (or It Did) causal propositions [ 141; given the observed outcome in the specified locus, they posit a cause for that outcome. Retrodictive causal propositions are particularly important in clinical medicine, where the individual patient is the subject of primary concern. Indeed, most diagnoses are cast as retrodictive causal propositions of the form: disease X is the cause of an observed constellation Y of symptoms and signs. The retrodictive causal proposition, “Smoking caused John’s lung cancer” is equivalent to the assertion that John would not have developed his specific type of lung cancer by the time he did had he not smoked. That is, if the proposition is true, smoking is a necessary (but not necessarily a sufficient) cause of John’s lung cancer. More generally, when we think about the causes of potentially repeatable outcomes like upper respiratory infection or myocardial infarction, “A caused John’s B” is equivalent to “B would not have happened as and when it did had John not been exposed to A”, where the nature of the exposure to A, the circumstances constituting “as”, and the allowable time window for “when” must all be specified. Thus, a retrodictive causal proposition is what philosophers term a counter-actual conditional: it asserts what would have followed something that in fact did not occur [15]. This implies two very important consequences. First, the meaning of a retrodictive causal proposition is not complete until the “alternative world” in which John was not exposed to A is described; in particular, whether or not the retrodictive causal proposition is true depends on this description. Second, whether or not a retrodictive causal proposition is true is inherently unobservable, since John was in fact exposed to A. John is a particular set of psychological and physical characteristics, shaped by his own particular history. Smoking is one component of this set, connected in innumerable ways to other components. How must we imagine a John who

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did not smoke? Would he be much heavier? Would he suffer from more or less stress? Would his coronary arteries be healthier? Probably the answers to none of these questions would affect the truth of the retrodictive causal proposition about his lung cancer, but what if John had died of a heart attack? Or suppose Maria developed agranulocytosis while receiving procainamide therapy for a severe arrhythmia. Because her arrhythmia was life-threatening, in an alternative world in which she received no medication for her condition, she might not have experienced the agranulocytosis simply because she died from her arrhythmia. This surely is not a compelling argument to impute causality for the agranulocytosis to the procainamide. But then does the question of whether or not the procainamide caused the agranulocytosis depend on a different alternative world in which Maria is assigned another antiarrhythmic drug? If so, what is that appropriate drug? In general, we face a dilemma. Because a retrodictive causal proposition is counterfactual, its meaning-and hence its truthdepends on the construction of an alternative world specifying what else would have been the case in the absence of exposure to the putative cause A. But the truth of the retrodictive causal proposition may depend on the particular alternative world we construct. So which alternative world is the right one for our purposes? Notice that, so far, we have not even considered the question of how we assess whether or not the retrodictive causal proposition is true, given that the real world is the only one we know about. The dilemma is about the meaning of a retrodictive causal proposition, and the dependence of that meaning on the selection of a single appropriate alternative world. To appreciate the distinction, suppose we could run time backwards and then actually experience any alternative world we imagined, so that we could observe whether the outcome B occurred in that world too. Which alternative world should we choose to determine whether A had caused B in the real world? To resolve the dilemma, we must think about the reasons why we entertain the causal proposition in the first place. Often we face a decision such that we would choose to act one way if the causal proposition were true and another way if it were false. For example, Maria’s physician would take her off procainamide if he believed that the procainamide

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caused her agranulocytosis; otherwise, he would continue with a therapy that was proving effective in controlling her arrhythmia. Or John’s son might be persuaded to stop smoking if he believed that smoking had caused his father’s lung cancer. For Maria’s doctor, the appropriate alternative world includes the alternative treatment for arrhythmia he would prescribe should he decide to stop the procainamide; for John’s son, the alternative world is his projection of his own life, with smoking halted at his present age (assuming that his smoking history and other relevant characteristics are the same as his father’s at the same age). This principle applies to all three types of causal proposition; when a causal proposition interests us because we face a decision to which it is related, the choice of alternative world will be determined by the actions available to us. As such, the meaning of a causal proposition should not be separated from the uses to which the proposition will be put. This dependence of the meaning of a causal proposition on its contemplated use is of fundamental importance and extends beyond the problem of choosing an appropriate alternative world. For example, why did Maria’s physician concern himself with procainamide rather than Maria’s genotype as the cause of her agranulocytosis? Formally, either could serve as the putative cause A in a retrodictive causal proposition; and both the resulting retrodictive causal propositions might in fact be true. The retrodictive causal proposition framed by Maria’s physician is simply more relevant to the decision problem he faced, since there was nothing he could do to alter her genotype. In the future-for example, if the mechanism of procainamide-induced agranulocytosis is found to depend on an enzymatic defect that could be remedied by infection with a virus capable of supplying the correct DNA coding sequencethe reverse might be the case. Potential and predictive causal propositions Potential (or It Can) causal propositions such as “Smoking can cause lung cancer” refer to a population locus and a past or future outcome. In essence, a potential causal proposition asserts that there exists (or will exist) at least one individual in a particular population for whom the relevant retrodictive causal proposition is (will be) true [14]. Potential causal propositions appear frequently in the epidemiologic literature because they reject the standard epidemiologic null hypothesis that a given factor is unrelated

to a given outcome. Much of the epidemiologic discussion of causality has focused on criteria for assessing It Can causality, including such elements as the strength, biologic gradient, unbiasedness, statistical significance, specificity, consistency, and biologic plausibility and coherence of the exposure-outcome association [24]. But for many questions, it is not sufficient just to know that a given cause can produce a particular effect; rather, it is important to know how jirequentfy it does so. This question is addressed by predictive (or It Will) causalpropositions [14], which refer to a future outcome in either an individual or a population. We call these propositions predictive, since they estimate the proportion of individuals in a given population (or equivalently, the probability for an individual randomly selected from that population) who will experience the given effect due to the given cause. Thus, prognoses in clinical medicine may take the form of predictive causal propositions. Also, predictive causal propositions are essential to risk-benefit analyses for public health decision making. It is essential that the locus (specific individual or population) to which a predictive causal proposition refers be specified exactly. For example, while a potential causal proposition that is true for population P is also true for any larger population that contains P, the truth (or perhaps accuracy) of a predictive causal proposition is inherently relative to a particular, specified population. We now see how complicated the meaning of a “population-based” potential or predictive causal proposition must be. In principle, such a proposition should include a specification of the relevant reference population, descriptions of the exposure A and the “as” and “when” limits on the outcome B, and an alternative world for every member of the population. Thus, potential causal propositions and predictive causal propositions (for populations) have meanings whose complexity increases with the size and structure of the populations to which they refer, and these propositions can be understood by clearly understanding the meaning of the underlying component retrodictive causal propositions. HOW SHOULD CAUSAL PROPOSITIONS BE ASSESSED?

Regardless of which of the three causal propositions is being addressed, it can rarely be determined as certainly true or certainly false.

Causal

Propositions

For example, the truth or falsity of a retrodictive causal proposition depends on what would have happened in the alternative world, which is inherently unobservable. Thus even in the individual-case retrodictive setting, causation is fundamentally different from association. Association is observable; causation inevitably involves a subjective inference about what might have been in the absence of the putative cause. Thus we cannot know “for sure” that any retrodictive causal proposition is true, in the same sense that we can be certain that Harold is taller than his wife. Nor can we know “for sure” that a retrodictive causal proposition is false, except in the trivial case in which we observe that the individual in question was certainly not exposed to the putative cause. Given exposure to the putative cause, retrodictive causal propositions can be neither verified nor falsified with certainty. Rather, the problem is essentially one of quantifying our uncertainty about what would have happened in an unobservable alternative world. Our opinions about this alternative world must be based upon analogies between that world and events observable in the one we actually inhabit. For example, Paul travelled to Africa last year, took chloroquine as prophylaxis for malaria, and returned in good health. For this year’s trip, he receives amodioquine and develops hepatitis. Last year’s experience provides an analogy for what would have happened this year had Paul not taken amodioquine, but of course the analogy is far from perfect, since, for example, the possibilities of exposure to viral hepatitis are certainly not identical from one African journey to another, nor did Paul’s alcohol consumption, nutritional status, or the state of his immune system, all of which might affect his propensity to develop hepatitis from different “causal agents”, necessarily remain constant from last year to this. The task of assessing a given retrodictive causal proposition involves developing such analogies, constructing arguments and amassing evidence relating to their strength, and then determining the plausibility of the proposition in the light of these arguments and evidence. Tools appropriate for such a task must take explicit account of the uncertainty introduced by the use of inherently indirect analogies, and in particular they must be capable of expressing the output of the causality assessment in such a way that the residual effects of this uncertainty are readily apparent.

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Probability theory, with probability interpreted as a quantitative measure of subjective degree of belief [ 141,offers a powerful set of such tools. We have applied such a probabilistic approach to the causality assessment of adverse drug reactions [l&l 81. In this approach, the aim of retrodictive causality assessment is to evaluate the assessor’s probability that exposure to a particular drug D caused a particular adverse event E in a particular patient, given all relevant case and background information. The strategy uses the rules of probability theory like Bayes’ theorem and the Law of Total Probability to decompose this rather complex probability assessment problem into a string of other problems that involve propositions more accessible to the knowledge and experience of the expert assessor. Typically, the solution to these component problems can be anchored to available epidemiological data (the prior probability of causation is equivalent to the etiologic fraction among the exposed) and to credible models that describe the mechanism of the adverse event as a function of the various possible causes (to estimate the likelihood ratio). In the end, of course, these assessments simply give quantitative expression to the assessor’s beliefs; the hope is that her expertise makes these beliefs worth obtaining, and that the problem has been structured in such a way that the beliefs assessed are more solidly grounded than would be a global, unstructured causality assessment of the original retrodictive causal proposition. Potential and predictive causal propositions also require the acknowledgement of uncertainty in their assessment, coupled with an attempt to quantify that uncertainty. In the language of probability, the truth of a potential causal proposition relating exposure A and outcome B may be highly probable for a particular population, even though for no individual in the population exposed to A who experiences B is the relevant retrodictive causal proposition anywhere near certain. This is because the probability of the potential causal proposition is just the probability that at least one of the retrodictive causal propositions is true. It is also possible, however, and often desirable, to assess potential causal or predictive causal propositions without first assessing a series of retrodictive causal propositions, The epidemiologic approach to such an assessment is usually based on studies of the relative risk and risk difference in exposed vs unexposed

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groups, along with an evaluation of systematic bias and random error to help distinguish causation from mere association. But, as we shall argue in the next section, difficulties can arise in interpreting these assessments, especially if those who carry them out ignore the true meaning of the propositions whose causality is assessed. After all, how can we assess the truth of a proposition whose meaning is not well defined? SO WHAT? PITFALLS

AND CONSEQUENCES

Clinicians, epidemiologists, and other health scientists frame causal propositions in order to organize their clinical experience and scientific knowledge into a form that serves to guide clinical decisions and public health policies. As illustrated by the following four pitfalls, the quality of these decisions and policies can suffer if health workers ignore the issues that we have raised. 1. Causality assessment criteria do not define causality. As noted above, epidemiologists have evolved a set of criteria to assess whether or not a given exposure can cause a particular outcome. These criteria have included such elements as minimization of analytic bias (including assurance that exposure preceded the outcome), strength of association, the biologic gradient (“dose-response” relationship), statistical significance, consistency, and biologic plausibility and coherence [2-4]. These criteria are valuable tools for distinguishing causal from non-causal associations in populations and thus for evaluating the epidemiologic evidence bearing on a potential causal proposition; usually, when the evidence from a particular study satisfies them, the probability that A can cause B increases, and when the evidence does not, this probability decreases. Hill, Susser, and others recognize that these criteria do not define cause per se, but merely provide guidelines for assessing it. Nonetheless, the criteria are occasionally used by epidemiologists and clinical researchers as though they provide an operational definition of causality: A causes B if and only if there exist studies for which the criteria are fulfilled. But this substitution of assessment criteria for the underlying meaning of causality can produce incorrect causal inferences. In particular, there can be circumstances under which the criteria are not met, but for which the potential causal proposition between A and B is true.

For example, cow milk intolerance has been blamed for a variety of adverse clinical outcomes in infants and young children, including colic and sleep disturbances. Repeated placebo-controlled, double-blind challenges and dechallenges have yielded extremely convincing evidence of a causal role for cow milk intolerance in some infants and children with these problems. Thus “for some” individuals, the relevant retrodictive causal propositions are practically certain, and so the relevant potential causal proposition for populations containing such individuals must also be true. Yet the causality criteria fail in this example. The magnitudes of the association determined from population-based studies are likely to be very weak, because the outcomes+olic and sleep disturbance-have many alternative causes, the exposure to cow milk is extremely common, and only a few children are susceptible to cow milk intolerance. [As Rothman [9] has pointed out, the fact that a given cause is weak may have little biologic significance if accompanying, “component” causes (e.g. susceptibility to cow milk intolerance) are rare.] As a consequence, studies of the effect of cow milk elimination diets in children with these problems have yielded inconsistent results [ 19-251. And because the mechanism is likely to be allergic, no dose-response relationship has been (or could be) found. 2. Assessing the wrong type of causal proposition. The basic epidemiologic strategy to establish that exposure A is causally connected to outcome B is to document, for some study population, a relative risk associated with exposure statistically significantly exceeding unity. This strategy, of course, addresses the potential (Zt Can) proposition, and the resulting “proved” causal proposition is a potential causal proposition. Unfortunately, potential causal propositions are often applied to decisions for which a predictive causal proposition or retrodictive causal proposition would in fact be appropriate, and the adverse consequences of this confusion can be considerable. For example, case reports [2&28] and epidemiologic studies [29-361 rendered quite plausible the potential causal proposition connecting pertussis vaccination in children with encephalopathy and other serious neurologic events in the first few days following immunization. As a result, pertussis immunization decreased dramatically in the United Kingdom [37], Japan [38, 391, and Sweden [40] in the late

Causal Propositions

1970s and early 1980s. In each of these settings, the change in immunization practice was followed by a large increase in the incidence of pertussis [3742], including many fatalities and neurologic complications of the disease. Of course, clinical and public health decisionmaking about the use of the vaccination should have been guided by the relevant predictive causal propositions introduced into risk-benefit calculations. Such calculations have more recently been carried out, and their results all indicate a lower risk of adverse health outcomes with pertussis immunization than without [36,41,43347]. The same kind of situation arises in the context of drug regulation, where all too often in the past regulatory authorities have responded to a plausible potential causal proposition with an inappropriate decision to curtail the use of a beneficial medication 1481,a decision that should properly have been based on a predictive causal proposition, formulated for various differentially affected populations of drug users [49]. Potential causal propositions may also be inappropriately substituted for retrodictive causal propositions. For example, a clinician who observes an adverse event in a patient taking one or more medications will often consult a pharmacology text or some other standard source, such as the Physician’s Desk Reference (U.S.) or the Compendium of Pharmaceuticals and Specialties (Canada). Such sources list adverse events that have been previously noted in patients taking the drug, either in clinical trials during drug development or as “spontaneous” case reports that accumulate after the drug has been marketed. Such lists rarely indicate the frequency with which such events are noted (a frequency relevant to the predictive causal proposition), which would be helpful to the clinician in deciding whether to prescribe a particular drug. But the lists also offer little help in addressing the It Did proposition, i.e. the retrodictive causal proposition for the particular patient experiencing the adverse event for whom the physician must formulate a proper treatment strategy. In addition, medicolegal battles usually concern the truth or falsity of a particular retrodictive causal proposition. For example, an infertile woman who brings suit against a manufacturer of intrauterine devices (IUDs) will usually summarize her claim that the company’s IUD caused her infertility by pointing out that: (1) she is infertile; (2) she used the

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company’s IUD; and (3) epidemiologic studies have been virtually unanimous in indicating an increased risk of infertility in women who use the devices. Points (1) and (2) are usually not subject to dispute. Rather, most of the debate revolves around the strength of the epidemiologic evidence. The plaintiff’s attorney usually cites the epidemiologic studies indicating an elevated risk, whereas the defendant will often engage the services of one or more expert witnesses to criticize those same epidemiologic studies on methodologic or statistical grounds, or he will cite studies with negative findings. Thus, most of the evidence presented bears on the potential causal proposition associating the devices with infertility. While it is true that the relevant potential causal proposition is certainly more probable than any single retrodictive causal proposition, a piecewise attack on the quality of the individual studies that claim plausibility for the potential causal proposition rarely has much bearing on whether, given all the evidence together, the potential causal proposition is very likely to be true (see the discussion below on the role of uncertainty in causality assessment). And even if the potential causal proposition is virtually certain, the retrodictive causal proposition could still have negligible probability, depending on the information about the particular patient (her age, gynecological history, exposure to other factors associated with infertility, etc.) and the relative frequencies of IUD-induced and other causes of infertility. 3. Specljication of alternative worlds. In an earlier section, we defined the retrodictive causal proposition in terms of a counterfactual conditional based on an imaginary “alternative world” in which the same individual was not exposed. When considering potential or predictive causal propositions for populations, rather than individuals, the causal proposition should (at least in principle) include a specification of the alternative world for every member of the population. In practice, of course, this is infeasible, and various alternative strategies are employed to ensure exchangeability (comparability) in the aggregate for the exposed population. For example, a randomized clinical trial can be thought of as a strategy in which participants exposed to the control treatment are exchangeable in an aggregate sense (i.e. on average) with subjects exposed to the experimental treatment. The distribution of outcomes in the control

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group can then be thought of as a reasonable proxy for the distribution that would have been obtained had the subjects in the experimental group received the control treatment. The closeness of this type of proxy becomes more problematic in dealing with nonexperimental (observational) studies, such as case-control studies. Whereas randomized trials usually compare an experimental treatment to some alternative therapeutic option for the types of patients being treated (which might include no treatment), the typical strategy in case-control studies (and the multivariate models like logistic regression that are used for analyzing their results) is predicated on comparing exposure vs non-exposure. In many cases, non-exposure provides an inappropriate alternative world for exposure, particularly when the exposure involves some form of medical treatment. For example, in a recent large multicenter case-control study of agranulocytosis and aplastic anemia [50], a number of non-steroidal anti-inflammatory drugs were examined for their relative risks vs no treatment. For some clinical indications in certain categories of patients (e.g. fevers or minor aches and pains in young, otherwise healthy adults), one reasonable treatment choice is no treatment. But for many of the clinical indications under study, particularly the rheumatic diseases, withholding treatment is not an acceptable (i.e. ethical) therapeutic option. Thus for patients with such diseases, the causal proposition being evaluated is inherently unhelpful from the standpoint of rational decision-making [5 11. Finally, multivariate models may perform badly when alternative worlds involve isolated changes in a single exposure, since any realistic alternative world that might be contemplated would include correlated changes in other factors that could affect the outcome. For example, does the coefficient for smoking in a multiple logistic regression model of cardiovascular mortality truly reflect the average relative risk in smokers vs otherwise similar non-smokers? If the subjects in the study sample had not smoked, for example, they might well have been more overweight or under greater stress or subjected to other experiences or changes that would have had adverse effects on their cardiovascular risk. Such individuals are not necessarily exchangeable with subjects who never smoked. In other words, non-smokers of the same weight, subject to the same stresses, and similar for other risk factors may not

adequately represent the appropriate alternative world for smokers had they not smoked. 4. The role of uncertainty in causality assessment. As argued above, many clinicians and scientists appear to seek definitive “true” or “false” determinations to causal propositions. One recent example is the debate about exposure to aspirin as a cause of Reye’s syndrome in children with antecedent viral illnesses. Casecontrol studies from the early 1980s indicated a strong association (odds ratios above 10) between aspirin exposure and the development of Reye’s syndrome [52-541. Nonetheless, several sources of possible bias (based on how cases and controls were selected, how histories of medication exposure were elicited, and potentially confounding differences between cases and controls) were evident in these early studies [55, 561. Critics who emphasized the methodologic weaknesses of these “true” (i.e. positive) studies seemed quite ready to accept the alternative assessment (“false”) to the It Can proposition of aspirin’s causal role. Defenders of the studies, on the other hand, argued that the unusually strong association (high odds ratios) was unlikely to be explained on the basis of these potential sources of bias. These defenders appeared quite ready to accept the truth of the It Can proposition for aspirin and Reye’s syndrome. In fairness to the critics, few have claimed that the evidence is sufficient to be certain that aspirin is not a cause of Reye’s syndrome. But the critics’ underlying ethos appears to be that in the absence of absolute certainty that a causal proposition is true, the alternative is, if not false, at least unproven. Thus, such critics dichotomize the assessment of the It Can proposition as either “certainly true” (which never occurs, since no epidemiologic study can be methodologically perfect) or “unproven”. But no attempts are made, either by critics or defenders, to quantify their uncertainty by estimating the probability that the causal proposition is in fact true. The search for the holy grail of certainty has been an unfortunate source of confusion, not only for clinicians and epidemiologists, but also for the consumers of medical and scientific knowledge, i.e. the general public. Lay persons generally find it quite frustrating to read in their local papers about a study whose results contradict those of a similar study published on the same subject in the previous year. To be sure, an inability or unwillingness to see shades of grey

Causal

Propositions

is not entirely the fault of the scientific community. Many people prefer simple answers to complex questions. The fact that for any causal proposition, we can be certain about neither its truth nor falsity is discomfiting to many lay persons and scientists alike. The seeking of black-and-white answers to complex questions is probably behind epidemiologists’ rekindled interest in the philosophy of Popper and his followers. According to the Popperians, mathematics is the highest science, since mathematical theorems can be proved or disproved with virtual certainty once a set of initial axioms has been assumed. The parallel in causal inference is to start with a theory, i.e. a given causal proposition, and then, repeatedly and in a variety of ways, design and carry out specific studies capable of falsifying the theory. Causal propositions that have withstood attempts at falsification, while not certain to be true, can be tentatively accepted as the basis for decision-making, while those that have been shown to be false should be rejected. As with any other kind of evidence, however, questions can always be raised about the roles of chance and bias in producing a result that is inconsistent with (“falsifies”) a given causal proposition. For example, a recently published case series [57] reporting a low rate of prior aspirin exposure in children with Reye’s syndrome provides no convincing evidence of falsification, because exposure histories were based exclusively on extraction of medical records rather than the in-depth interviews that were used in both older [52-541 and more recent [58-601 case-control studies. Thus it is not clear why evidence against a hypothesis should have any greater weight than evidence for that hypothesis. Falsification and verification are both inherently subjective. Neither provides certainty about the truth or falsity of any causal proposition. We need to acknowledge and, even better, to quantify our degree of uncertainty as a basis for decision-making.

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understanding of what a casual proposition means, as well as an appreciation of the distinctions between retrodictive, potential, and predictive causal propositions, may therefore be helpful to researchers and practitioners alike. Confusion about what causal propositions mean vs how we typically assess them can impair communication among epidemiologists, as well as between them and clinical practitioners. Moreover, pitfalls in specifying the appropriate comparison (“alternative world”) that gives meaning to any causal proposition, and the misguided notion that causal inference involves a black-and-white decision by which causality can be “proved” or “disproved”, have led to problems in the conduct, analysis, and interpretation of epidemiologic and clinical research. These pitfalls can lead to errors in clinical or public health decision-making and subsequent disillusionment with the clinical or scientific communities supplying the “evidence”. We believe that researchers and clinicians can avoid such pitfalls by using the framework we have presented: (1) to decide whether a given causal question pertains to an individual or a population; (2) to distinguish three types of causal propositions; retrodictive: (It Did), potential (It Can), and predictive (It Will); (3) to define the relevant causal proposition in a useful decision context by specifying an appropriate “alternative world”; and (4) to assess the truth of the relevant causal proposition probabilistically, rather than all-or-none. In addition, we hope that the issues we have raised will stimulate further discussion about the type, meaning, and assessment of specific causal propositions. Acknowledgements-Drs Jean-Francois Boivin, Iain Chalmers, Harry Guess, Tom Hutchinson, Olli Miettinen, and Mervyn Su ser provided thoughtful and helpful comments on earhe.9 verstons of this manuscript. This work was carried out when Dr Kramer was a senior career investigator of the Fonds de la Recherche en Sante du Quebec. Dr Lane was partially supported by National Science Foundation Grant No. NSFIDMF-891154801.

CONCLUSION

Causal inference is an essential intellectual activity throughout the health sciences, from the clinician’s diagnosis of the cause of an individual patient’s symptoms and signs, through the epidemiologist’s case-control study of determinants of a chronic disease, to the clinical trialist’s randomized controlled experiment comparing clinical interventions. An

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