Abstract While the futures–spot price relationship is well established in commodity markets, the transmission of price signals to the retail level remains an " incomplete bridge, ” particularly under varying speculative regimes. Traditional empirical approaches often fail to capture the nonlinear and heterogeneous dynamics of this process, typically providing a single Average Treatment Effect (ATE) that masks the distortions caused by market frictions. This study addresses this gap by developing a novel causal machine learning (CML) framework. Leveraging double machine learning (DML), we isolate the causal link between futures and retail prices by " partialing out ” high-dimensional confounding variables, effectively distinguishing the informational signal from the market " noise ” identified in recent literature. We illustrate this framework using the US frozen concentrated orange juice (FCOJ) market as a functional laboratory for concentrated and volatile ” soft ” commodities. Our results reveal a non-monotonic relationship: while moderate speculation enhances price discovery (CATE ≈ 1), both low and high speculative intensity impair signal propagation. Crucially, we find that excessive speculation leads to ” informational decoupling, ” where increased statistical uncertainty in the CATE reflects a coordination failure in firm-level pricing and procurement decisions. These findings challenge the assumption that speculation consistently enhances market efficiency and could provide a robust, scalable, data-driven analytical tool for future supply-chain research and policy discussions on commodity market regulation.
Abeltino et al. (Wed,) studied this question.