Hao Duong Le

Hao Duong Le, Xin Xia, Zhang Chen

Causal discovery aims to identify causal relationships between variables and is a critical research area in machine learning. Traditional methods focus on statistical or machine learning algorithms to uncover causal links from structured data, often overlooking the valuable contextual information provided by metadata. Large language models (LLMs) have shown promise in creating unified causal discovery frameworks by incorporating both structured data and metadata. However, their potential in multi-agent settings remains largely unexplored. To address this gap, we introduce the Multi-Agent Causal Discovery Framework (MAC), which consists of two key modules: the Debate-Coding Module (DCM) and the Meta-Debate Module (MDM). The DCM begins with a multi-agent debating and coding process, where agents use both structured data and metadata to collaboratively select the most suitable statistical causal discovery (SCD) method. The selected SCD is then applied to the structured data to generate an initial causal graph. This causal graph is transformed into causal metadata through the Meta Fusion mechanism. With all the metadata, MDM then refines the causal structure by leveraging a multi-agent debating framework. Extensive experiments across five datasets demonstrate that MAC outperforms both traditional statistical causal discovery methods and existing LLM-based approaches, achieving state-of-the-art performance.

Hao Duong Le, Yifei Gao, Huan Li et al.

New-user cold-start is a critical bottleneck for e-commerce platforms: predicting user lifetime value (LTV) and conversion rate (CVR) for users with sparse interaction history. Two prior directions -- LLM-based semantic augmentation and learning using privileged information (LUPI) -- each face a key limitation. First, LLM augmentation produces unstructured rationales that are noisy and hard to operationalize in production. Second, naive student-teacher distillation can be brittle due to an information gap between the privileged teacher and the sparse student; moreover, this gap is heterogeneous across users. We propose SemRaD, a Semantic Reasoning-aware Distillation framework addressing both limitations. First, a Structured Semantic Reasoning Pipeline replaces free-form rationales with a structured schema built via a discover-curate-audit workflow, producing per user a Densified Semantic Profile (consumed by the deployed student via a Semantic-Gated Encoder that focuses on the most informative dimensions) and a Hindsight Distillation Target reconciled from pre- and post-conversion reasoning (used only at training). Second, to bridge this gap and handle its heterogeneity, a Hindsight-Aware Distillation Network transfers privileged knowledge via the hindsight target, with Distillation Experts improving transfer under per-user variability. On a large-scale industrial dataset, SemRaD lifts +1.9% LTV (Gini) and +1.0% CVR (AUROC) over a production-grade base; a four-week online A/B at Keeta confirms +1.0% LTV / +0.43% CVR. SemRaD also matches the production system's LTV using only 9% of the training data while improving CVR by 0.8%.