Hills Criteria of Causation outlines the minimal conditions needed to establish a causal relationship between two items. These criteria were originally presented by Austin Bradford Hill (1897-1991), a British medical statistician as a way of determining the causal link between a specific factor (e.g., cigarette smoking) and a disease (such as emphysema or lung cancer). Hill's Criteria form the basis of modern epidemiological research, which attempts to establish scientifically valid causal connections between potential disease agents and the many diseases that afflict humankind. While the criteria established by Hill (and elaborated by others) were developed as a research tool in epidemiology, they are equally applicable to sociology, anthropology and other social sciences, which attempt to establish causal relationships among social phenomena. Indeed, the principles set forth by Hill form the basis of evaluation used in all modern scientific research. While it is quite easy to claim that agent "A" (e.g., smoking) causes disease "B" (lung cancer), it is quite another matter to establish a meaningful, statistically valid connection between the two events. It is just as necessary to ask if the claims made within the social and behavioral sciences live up to Hill's Criteria as it is to ask the question in epidemiology (which is also a social and behavioral science). While it is quite easy to claim that population growth causes poverty or that globalization causes underdevelopment in Third World countries, it is quite another thing to demonstrate scientifically that such causal relationships exist. Hill's Criteria simply provides an additional valuable measure by which to evaluate the many theories and hypotheses proposed within the social sciences.
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Hill's Criteria
Hills Criteria will be presented here as they have been applied in epidemiology. Their application to research in the social and behavioral sciences will be discussed in class.
1. Temporal Relationship: Exposure always precedes the outcome. If factor "A" is believed to cause a disease, then it is clear that factor "A" must necessarily always precede the occurrence of the disease. This is the only absolutely essential criterion.
2. Strength: This is defined by the size of the association as measured by appropriate statistical tests. The stronger the association, the more likely it is that the relation is causal. For example, the more highly correlated hypertension is with a high sodium diet, the stronger is the relation between sodium and hypertension. Similarly, the higher the correlation between patrilocal residence and the practice of male circumcision, the stronger is the relation between the two social practices.
3. Dose-Response Relationship: An increasing amount of exposure increases the risk. If a dose-response relationship is present, it is strong evidence for a causal relationship. However, as with specificity, the absence of a dose-response relationship does not rule out a causal relationship. A threshold may exist above which a relationship may develop. At the same time, if a specific factor is the cause of a disease, the incidence of the disease should decline when exposure to the factor is reduced or eliminated. An anthropological example of this would be the relationship between population growth and agricultural intensification. Other things being equal, as population growth increases within a given area, we should see a commensurate increase in the amount of energy and resources invested in agricultural production. Conversely, when a population decrease occurs, we should see a commensurate reduction in the investment of energy and resources per acre. The same analogy can be applied to the current debate on global warming. If increasing levels of CO2 in the atmosphere is the cause of increasing global temperatures, then "other things being equal", we should see both a commensurate increase and a commensurate decrease in global temperatures following an increase or decrease respectively in CO2 levels in the atmosphere.
4. Consistency: The association is consistent when results are replicated in studies in different settings using different methods. That is, if a relationship is causal, we would expect to find it consistently in different studies and in different populations. This is why numerous experiments have to be done before meaningful statements can be made about the causal relationship between two or more items. For example, it has taken thousands of highly technical studies of the relationship between cigarette smoking and cancer before a definitive conclusion can be made that cigarette smoking increases the risk of (but does not cause) cancer. Similarly, it would require numerous studies of the difference between male and female performance of a specific behavior (e.g., cognitive tasks, domestic violence, nurturing activities, etc.) by a number of different researchers and under a variety of different circumstances before a conclusion could be made regarding whether a gender difference exists in the performance of such behaviors.
5. Plausibility: The association agrees with currently accepted understanding of pathological processes. However, studies that disagree with established understanding of biological processes may force a reevaluation of accepted beliefs. In other words, there needs to be some theoretical basis for making an association between a vector and disease, or one social phenomenon and another. One may, by chance, discover a correlation between the price of bananas and the election of dog catchers in a particular community, but there is not likely to be any logical connection between the two phenomena. On the other hand, the discovery of a correlation between population growth and the incidence of warfare among Yanomamo villages would fit well with ecological theories of conflict under conditions of increasing competition over resources.
6. Consideration of Alternate Explanations: In judging whether a reported association is causal, it is necessary to determine the extent to which researchers have taken other possible explanations into account and have effectively ruled out such alternate explanations. In other words, it is always necessary to consider multiple hypotheses before making conclusions about the causal relationship between any two items under investigation.
7. Experiment: The condition can be altered (prevented or ameliorated) by an appropriate experimental regimen.
8. Specificity: This is established when a single putative cause produces a specific effect. This is considered by some to be the weakest of all the criteria. The diseases attributed to cigarette smoking, for example, do not meet this criteria. When specificity of an association is found, it provides additional support for a causal relationship. However, absence of specificity in no way negates a causal relationship. Because outcomes (be they the spread of a disease, the incidence of a specific human social behavior or changes in global temperature) are likely to have multiple factors influencing them, it is highly unlikely that we will find a one-to-one cause-effect relationship between two phenomena. Causality is most often multiple. Therefore, it is necessary to examine specific causal relationships within a larger systemic perspective.
9. Coherence: The association should be compatible with existing theory and knowledge. In other words, it is necessary to evaluate claims of causality within the context of the current state of knowledge within a given field. What do we have to sacrifice about what we currently know in a given area in order to accept a particular claim of causality. What, for example, do we have to reject of our current knowledge in geography, physics, biology and anthropology in order to accept the Creationist claim that the world was created as specified in the Bible a few thousand years ago? Similarly, how consistent are racist and sexist theories of intelligence with our current understanding of how genes work and how they are inherited from one generation to the next? However, as with the issue of plausibility, research that disagrees with established theory and knowledge are not automatically false. They may, in fact, force a reconsideration of accepted beliefs and principles. All currently accepted theories, including Evolution, Relativity and non-Malthusian population ecology, were at one time new ideas that challenged orthodoxy. Thomas Kuhn has referred to such changes in accepted theories as "Paradigm Shifts".
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