Recent reviews of epidemiological evidence on the relation between exposure to diesel exhaust (DE) and lung cancer risk have reached conflicting conclusions, ranging from belief that there is sufficient evidence to conclude that DE is a human lung carcinogen (California EPA, 1994) to conclusions that there is inadequate evidence to support a causal association between DE and human lung cancer (Muscat and Wynder, 1995). Individual studies also conflict, with both increases and decreases in relative risks of lung cancer mortality being cited with 95% statistical confidence. On balance, reports of elevated risk outnumber reports of reduced risk. This paper reexamines the evidence linking DE exposures to lung cancer risk. After briefly reviewing animal data and biological mechanisms, it surveys the relevant epidemiological literature and examines possible explanations for the discrepancies. These explanations emphasize the distinction between statistical associations, which have been found in many studies, and causal associations, which appear not to have been established. Methodological threats to valid causal inference are identified and new approaches for controlling them are proposed using recent techniques from artificial intelligence (AI) and computational statistics. These threats have not been adequately controlled for in previous epidemiological studies. They provide plausible noncausal explanations for the reported increases in relative risks, making it impossible to infer causality between DE exposure and lung cancer risk from these studies. A key contribution is to show how recent techniques developed in the AI-and-statistics literature can help clarify the causal interpretation of complex multivariate data sets used in epidemiological risk assessments. Applied to the key study of Garshick et al. (1988), these methods show that DE concentration has no positive causal association with occupational lung cancer mortality risk.