The digital and computational revolutions have improved the prospects for analyzing the dynamics of large groups of interacting individuals. Digital trace data provide the type of large-scale, time-stamped, and granular information on social interactions that is needed to feasibly conduct research on social influence in non-experimental settings and to distinguish social influence effects from the confounding effects of homophily. This chapter reviews three concrete ways in which machine learning can improve the estimation of social influence effects from observational digital trace data. These computational approaches (a) make high-dimensional information about individuals accessible for analysis, (b) infer latent confounders from the structure of large-scale social networks, and (c) facilitate large-scale annotation of measures that can serve as instruments for causal identification