Combustion emissions cause pro-atherosclerotic responses in apolipoprotein E-deficient (ApoE/⁻) mice, but the causal components of these complex mixtures are unresolved. In studies previously reported, ApoE⁻/⁻ mice were exposed by inhalation 6 h/day for 50 consecutive days to multiple dilutions of diesel or gasoline exhaust, wood smoke, or simulated "downwind" coal emissions. In this study, the analysis of the combined four-study database using the Multiple Additive Regression Trees (MART) data mining approach to determine putative causal exposure components regardless of combustion source is reported. Over 700 physical-chemical components were grouped into 45 predictor variables. Response variables measured in aorta included endothelin-1, vascular endothelin growth factor, three matrix metalloproteinases (3, 7, 9), metalloproteinase inhibitor 2, heme-oxygenase-1, and thiobarbituric acid reactive substances. Two or three predictors typically explained most of the variation in response among the experimental groups. Overall, sulfur dioxide, ammonia, nitrogen oxides, and carbon monoxide were most highly predictive of responses, although their rankings differed among the responses. Consistent with the earlier finding that filtration of particles had little effect on responses, particulate components ranked third to seventh in predictive importance for the eight response variables. MART proved useful for identifying putative causal components, although the small number of pollution mixtures (4) can provide only suggestive evidence of causality. The potential independent causal contributions of these gases to the vascular responses, as well as possible interactions among them and other components of complex pollutant mixtures, warrant further evaluation.