Multimodal aspect-based sentiment classification (MABSC) requires fine-grained reasoning over both textual and visual content to infer sentiments toward specific aspects. However, existing methods often rely on superficial correlations—particularly between aspect terms and sentiment labels—leading to poor generalization and vulnerability to spurious cues. To address this limitation, we propose DPCI, a novel Dual-Path Counterfactual Integration framework that enhances model robustness by explicitly modeling counterfactual reasoning in multimodal contexts. Specifically, we design a dual counterfactual generation module that simulates two types of interventions: replacing aspect terms and rewriting descriptive content, thereby disentangling the spurious dependencies from causal sentiment cues. We further introduce a sample-aware counterfactual selection strategy to retain high-quality, diverse counterfactuals tailored to each generation path. Finally, a confidence-guided integration mechanism adaptively fuses counterfactual signals into the main prediction stream. Extensive experiments on standard MABSC benchmarks demonstrate that DPCI not only achieves state-of-the-art performance but also significantly improves model robustness.