The abductive natural language inference task (Abductive NLI) seeks to select more plausible hypothetical events based on given antecedent events and consequent events. However, inherent biases such as “logical defects” and “single-sentence label leakage” stemming from mediator and confounding variables in the inference process pose challenge. To address these issues, this paper proposes a novel knowledge-aware debiased inference model integrating intervention and counterfactual (KDIC). The model comprises three key modules: the mediator modulation module, the hypothesis-only bias module, and the external knowledge fusion module. Firstly, the mediator modulation module consists of causal graph intervention and hypothesis contrast learning. Causal graph intervention constructs a potential causal graph from given events and then extracts mediator variables, standing for the potential feature of unobserved events, via self-attention mechanism and graph convolutional network for guiding deep encoding. Concurrently, hypothesis contrast learning encourages the model to discern key factors affecting hypothesis judgment, rectifying logical inconsistencies. Secondly, the hypothesis-only bias module addresses the counterfactual problem by proactively identifying the inference biases arising from “single-sentence label leakage”. This module reduces the model’s reliance on specific words or phrases in the hypothesis, thereby enhancing robustness. Finally, this paper leverages a pre-trained common sense knowledge graph encoder, ComET, within the external knowledge fusion module. This integration enriches the model’s understanding of observed events’ motivations and potential outcomes, bolstering logical coherence across events. Experiments results on the αNLI dataset demonstrate that the inference ability of KDIC is second only to Electra-large-discriminator trained via self-supervised learning. However, KDIC exhibits greater robustness to alleviate biases in the inference process.