<p>How do individuals process political information? What behavioral mechanisms drive partisan bias? In this paper, we evaluate the extent to which partisan bias is driven by affect or ideology in a three-pronged approach informed by both psychological theories and recent advances in methodology. First, we use a novel survey experiment designed to disentangle the competing mechanisms of ideology and partisan affect. Second, we leverage multidimensional scaling methods for latent variable estimation for both partisan affect and ideology. Third, we employ a principled machine learning method, causal forest, to detect and estimate heterogeneous treatment effects. Contrary to previous literature, we find that affect is the <i>sole moderator</i> of partisan cueing processes, and only for out-party cues. These findings not only contribute to the literature on political behavior, but underscore the importance of careful measurement and robust subgroup analysis.</p>