Identifying the mechanisms behind the trends and variability in observations of ocean oxygen concentration (O<sub>2</sub>) is key to understanding long-term ocean deoxygenation. But this task remains challenging, as observations contain persistent signals from interacting physical and biogeochemical processes. Here we apply the time series causal discovery method, PCMCI, to a dataset of O<sub>2</sub> from the Oyashio region in the North West Pacific to test whether a causal relationship from the nodal cycle in the tidal forcing on to the O<sub>2</sub> exists. We identify causal links from the tidal index to O<sub>2</sub> and apparent oxygen utilisation (AOU), concentrated between approximately 27.35<em>σ<sub>θ</sub></em> and 26.75<em>σ<sub>θ</sub></em>, with a characteristic lag of approximately two years. The tidal effect on O<sub>2</sub> is only present at densities greater than the density of the late-winter mixed layer (the base of the mixed layer), with a maximum causal link at around 27.1<em>σ<sub>θ</sub></em>. Little evidence is found for a causal relationship with the O<sub>2</sub> solubility. Compared to lagged correlation, PCMCI detects a reduced strength and density distribution of the tidal effect and removes much of the lag structure while retaining the characteristic 2 year lag. The density and lag structure are consistent with transport and enhanced tidal mixing from within the Sea of Okhotsk system and the Kurils, but we cannot identify a geographical location or the complete physical pathway of the causal effect with the existing observations. It may be the case that several causal models are compatible with the existing data. These results show the promise of causal inference and discovery methods within oceanographic problems.