arXiv AI
Jul 7
MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation
arXiv:2603. 25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.
DCRMTA: Deep Causal Representation Learning for Multi-Touch Attribution
The paper introduces DCRMTA, an end‑to‑end framework for deep causal representation learning in multi‑touch attribution (MTA). It addresses a flaw in existing deconfounding pipelines that discard user‑related causal signals by explicitly preserving the causal impact of user features. Using structural causal modeling and adaptive counterfactual attention, DCRMTA produces invariant user representations and achieves up to a 5.2% relative improvement in PR‑AUC on real industrial datasets, while offering robust Shapley‑based credit allocations across marketing channels.
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arXiv Machine Learning
Aug 14
Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization
arXiv:2608. 13461v1 Announce Type: new Abstract: Post-click conversion rate (CVR) is a key metric in various scenarios including e-commerce and advertising, reflecting the efficiency and user experience in the second stage of the conversion process.
arXiv Machine Learning
Jul 17
Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest
arXiv:2607. 14161v1 Announce Type: cross Abstract: Pinterest is where people turn inspiration into action as users browse ideas, then take steps toward realization, often by discovering shoppable content.
arXiv Machine Learning
Jun 2
Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework
arXiv:2602. 12972v2 Announce Type: replace-cross Abstract: In online advertising, marketing interventions such as coupons introduce significant confounding bias into Click-Through Rate (CTR) prediction.
arXiv AI
2d ago
Structure-agnostic Causal Representation Learning
arXiv:2610.00968v1 Announce Type: cross Abstract: Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods requi...
arXiv Machine Learning
1d ago
Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports
The paper introduces Multi-Task Anti-Causal learning (MTAC), a framework that estimates latent causes from observed effects by exploiting both task-invariant and task-specific structural dependencies. MTAC constructs a structural equation model that separates a shared backbone mechanism from task-specific deviations, then uses maximum a posteriori inference to reconstruct causes. Applied to urban event reconstruction—parking violations, abandoned properties, and unsanitary conditions—MTAC outperforms strong baselines on real data from Manhattan and Newark, achieving up to a 33.04% reduction in mean absolute error.
arXiv AI
Jun 17
FoundCause: Causal Discovery with Latent Confounders from Observational Data
arXiv:2606. 17516v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the need to recover directed structure and latent confounding without interventions.
arXiv AI
Aug 12
From Prediction to Incrementality: Causal Optimization for Large-Scale Targeting and Recommendation
arXiv:2608. 10182v1 Announce Type: cross Abstract: Large-scale targeting and recommendation systems are typically built around predictive scores fed into heuristic or local allocation.
arXiv Machine Learning
Aug 27
DCEO: Direct Causal Effect Optimization for Long-Term User Value Modeling in E-commerce Search
The paper introduces DCEO, a data‑driven framework that learns item‑level proxy scores directly aligned with long‑term user objectives in e‑commerce search. It aggregates these scores into a user‑level metric, measures alignment via relative causal effect, and uses an actor‑critic model to generate context‑dependent fusion weights for multiple objectives. Offline experiments and a 41‑day online A/B test show DCEO improves GMV by 0.36% over traditional proxies.
arXiv AI
Jul 24
Probabilistic Residual Learning for Online Recommendations
arXiv:2607. 20863v1 Announce Type: cross Abstract: Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items.
arXiv Statistics ML
2d ago
Pragmatic DML with AI-Learned Representations
arXiv:2610.01935v1 Announce Type: cross Abstract: Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We...
arXiv Machine Learning
Aug 21
RecPFN: Prior-Fitted Networks for In-Context-Based Recommendations
arXiv:2608. 19735v1 Announce Type: new Abstract: We introduce RecPFN, a prior-fitted network that brings in-context learning to sequential recommendation.