Causality, Causes, and Causal... Health Article

CAUSAL INFERENCE

The nature of cause must be agreed before it can be inferred. David Hume, an eighteenth-century Scottish philosopher, isolated three properties essential to cause. Freely translated, these are association (cause and effect occur together), time order (causes precede effects), and connection or direction (repeated demonstrable, hence predictable, linkages exist between cause and effect). Hume's analysis endures and has hardly been improved on.

Causal inference is a judgmental process, not a snapshot but a movie. Causal hypotheses arise either from observation, existing knowledge and inductive reason, or from intuition. Hypotheses enunciated beforehand (a priori) can be subjected to repeated tests that allow their elimination or survival in respect, successively, of the causal properties. For each, criteria (or guidelines, canons, or postulates) assist judgment. They can be grouped under five categories, some with subcategories: strength; specificity; consistency; predictive performance; and coherence. Each is useful depending on the property under test and the type and quality of available research evidence.

Association is judged by the presence and strength of probabilities based on preset expectations of variation (so-called chance occurrence) and by consistency upon replication. Given survival, tests for time-order rely on establishing the sequence of cause and effect; reversal assures elimination. Failing elimination, the acid test for the property of connection or direction is difficult indeed: It depends on the complete array of criteria, with all alternative explanations and confounding accounted for.

In counterpoint, Hume rejected the validity of inductive logic (generalization from assembled particular observations) and thereby created an enduring problem for causal inference. He could find, he said, no logical compulsion to believe the sun would rise tomorrow. For him, logic could not demonstrate "necessary connection" from cause to effect. Following Hume, Karl Popper in the twentieth century found proof of hypotheses (verification) beyond logical reach. His theory allowed conclusive rejection (falsification) alone. His epidemiological followers prosecuted an intense debate in support.

Popper, aiming to falsify hypotheses, insisted solely on deductive logic (prediction of particular outcomes from prior general hypotheses). He dismissed such longstanding counters to his view of science as Bacon's (1599) or John Stuart Mill's (1856) inductive logic. Mill spelled out inductive "canons" implicit in the casual inferences of much observational science, including epidemiology. Despite Popper's insistent rejection, all scientists practice induction. Obligatory pragmatists, many also recognize added value in Popper's "hypothetico-deductive" procedure.

Other perspectives exist in epidemiology. Kenneth Rothman espouses a Popper-like view of causal inference but also provides heuristic if nondynamic model of multiple "sufficient and component" causes. James Robins, Sander Greenland, and others, following one of Hume's ideas modeled by Jerzy Neyman in 1923, elaborate the "counterfactual" approach. Limiting causes to change agents, this excludes steady-state conditions as causative if not as context, and demands strict formulations amendable to mathematical logic. A thought experiment compares an entity after exposure to the same entity had there been no exposure, a comparison unattainable in practice. Instead, the outcome variable is adjusted statistically. Bayesian probability theory provides artillery for applications. Tests for counterfactuals in securing or dismissing uncertain causality in epidemiology are awaited with interest.

MERVYN W. SUSSER

(SEE ALSO: Epidemiology)

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