Despite widespread adoption of machine learning, many firms face the common challenge of relevant datasets being distributed amongst market competitors whom are reluctant to share information. Accordingly, recent works propose analytics markets as a way to provide monetary incentives for collaboration, where agents share features and are rewarded based on their contribution to improving the predictions of others. These contributions are determined by their relative Shapley value, computed by treating features as players and their interactions as a cooperative game. However, this setup is known to incite agents to strategically replicate their data and act under multiple false identities to increase their own revenue whilst diminishing that of others, which limits the viability of these markets in practice.
In this work, we develop an analytics market robust to such strategic replication for supervised learning problems. We adopt Pearl’s do-calculus from causal inference to refine the cooperative game by differentiating between observational and interventional conditional probabilities. As a result, we derive Shapley value-based rewards that deter replication by design.