Online platforms often have conflicting goals: they face tradeoffs between increasing efficiency and reducing disparities, where the latter may relate to objectives such as the longer-term health of the marketplace or the organization's mission. We examine how participants' profile pictures shape this trade-off in the context of a peer-to-peer lending platform. We develop and apply an approach to estimate marketplace participants' preferences for different profile features, distinguishing between (i) "type" (e.g., gender, age) and (ii) "style" (e.g., smiling in the photo). Relative to type, style features are easier to change, and platforms may be more willing to encourage such changes. Our approach starts by using causal inference methods together with computer vision algorithms applied to observational data to identify type and style features of profiles that appear to affect demand for transactions. We further decompose type-based disparities into a component driven by demand for certain types and a component that arises because different types have different distributions of style features
we find that style differences exacerbate type-based disparities. To improve internal validity, we then carry out two randomized survey experiments using generative models to create multiple versions of profile images that differ in one feature at a time. We then evaluate counterfactual platform policies based on the changeable profile features and identify approaches that can ameliorate the disparity-efficiency tension. We identify marketplace feedback effects, where encouraging certain style choices attracts participants who value these choices.