Counterfactual Explanations for Sequential Purchase Decision Modeling in E-Commerce: A Systematic Survey

Abstract

The growing complexity of e-commerce platforms necessitates recommendation systems that provide high-quality predictions while maintaining transparent and interpretable algorithms with opportunities for recourse. As users proceed through different stages of sequential interaction, from browsing and comparison to adding products to a basket and finally proceeding to checkout, the interpretation of the sequential decision-making process becomes more complicated than the interpretation of static recommendations. Based on a qualitative literature-selection approach applied to key electronic databases, this paper provides a survey of the existing primary research in the domains of XAI, causal reasoning, sequential recommendation, and algorithmic recourse for sequential purchase decisions in e-commerce. A taxonomy of the existing literature in the domain of interest is provided into five main approaches: optimization, search, generative, sequential decision, and causal relationships. The key constraints in e-commerce that an explanation technique must satisfy for successful practical application are analysed: temporal non-reversibility, changing catalogue, multi-level behaviour hierarchy, alignment of users' short- and long-term intentions, and KPIs. The comparative analysis shows that no existing paradigm meets all domain constraints. A conceptual model of the hybrid counterfactual reasoning system is proposed and further research directions are highlighted: ultra-low latency counterfactual inference, integration with LLMs, cold-start counterfactual recourse, and counterfactual robustness under concept drift.