Despite the remarkable success of large vision-language models (LVLMs) on various tasks, their susceptibility to knowledge bias inherited from training data hinders their ability to generalize to new scenarios and limits their real-world applicability. To address this challenge, we propose the Counterfactual Bias-Robust Reasoning (CoBRa) dataset that tackles knowledge bias by offering a novel collection of VQA examples designed to evaluate and mitigate bias in LVLMs. These examples encourage counterfactual thinking by providing edited knowledge graphs and image contents, with detailed annotations of reasoning processes to facilitate a comprehensive understanding of the examples. Based on the dataset, we introduce a Chain of Counterfactual Thought (CoCT) method that learns the bias-robust reasoning processes and provides in-context examples demonstrating how existing reasoning generalizes to counterfactual scenarios. This enables LVLMs to explicitly reason step-by-step rather than relying on biased knowledge, leading to more generalizable solutions. Our extensive evaluation demonstrates that CoCT outperforms existing approaches on tasks requiring reasoning under knowledge bias. Our work is available at https://github.com/SuperJohnZhang/CoBRa .