Causal Feature Discovery through Strategic Modification

As algorithmic decision-making takes a more and more important role in myriad application domains, incentives emerge to change the inputs presented to these algorithms--people may either invest in truly relevant attributes or strategically lie about their data. Recently, a collection of very interesting papers has explored various models of strategic behavior on the part of the classified individuals in learning settings, and ways to mitigate the harms to accuracy that can arise from falsified features [Dalvi et al., 2004, Brückner et al., 2012, Hardt et al., 2016, Dong et al., 2018]. Additionally, some recent work has focused on the design of learning algorithms that incentivize the classified individuals to make"good" investments in true changes to their variables[Kleinberg and Raghavan, 2019]. The present paper takes a different tack, and explores another potential effect of strategic investment in true changes to variables, in an online learning setting: we claim that interaction between the online learning and the strategic individuals may actually aid the learning algorithm in identifying causal variables. By causal, we mean, informally, variables such that changes in their true value cause changes in the true label and lead agents to improve.