  Many industries rely on data-driven analytics, yet useful datasets are often
distributed amongst market competitors that are reluctant to collaborate and
share information. Recent literature proposes analytics markets to provide
monetary incentives for data sharing, however many of these market designs are
vulnerable to malicious forms of replication -- whereby agents replicate their
data and act under multiple identities to increase revenue. We develop a
replication-robust analytics market, centering on supervised learning for
regression. To allocate revenue, we use a Shapley value-based attribution
policy, framing the features of agents as players and their interactions as a
characteristic function game. We show that there are different ways to describe
such a game, each with causal nuances that affect robustness to replication.
Our proposal is validated using a real-world wind power forecasting case study.
