I am using a decision tree to perform binary classification. I've found that a particular feature seems to be an important one; however, keeping it in my model doesn't yield better predictions (i.e. results in a near equivalent confusion matrix as that without the feature). Does this just mean that the feature is highly correlated with another/other feature(s) already in the model?
Yes, that seems to be the likely reason why you're not seeing an improvement in the model performance as you add that feature in your model.
In order to confirm this, you can take all features that are in the model, add the feature of interest, and perform a multicollinearity diagnostic (e.g., VIF) on that set of features. (As a first step though, you could just review pairwise correlations among these features to check if there's another single feature that is highly correlated with the feature of interest.)
Also, based on the tags in your question, it looks like you're using CART -- which splits each parent node into only two child nodes at a time. You may need to grow the tree further by splitting the tree multiple times with that same feature of interest. (Make sure that tree criteria -- like split size, leaf node size, and depth are not restricting the growth of the tree.) Alternatively, you can try other decision tree algorithms (e.g., CHAID) that go beyond binary splits.