Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge

2025-04-08

arXiv.org

AI Summary

This work addresses the limited effectiveness of personalized response generation in persuasive dialogue systems. Methodologically, we propose the first generative framework integrating causal discovery, counterfactual reasoning, and variational latent variable modeling: (1) causal discovery is employed to identify strategy-level causal structures between user tactics and system responses; (2) system responses are modeled as intervenable counterfactual actions; and (3) user latent states are jointly inferred to enable dynamic personalization. Our key contribution lies in being the first to embed causal inference and counterfactual intervention mechanisms into dialogue policy learning—explicitly modeling how persuasion outcomes would change under alternative responses. Experiments on a real-world social welfare dataset demonstrate statistically significant improvements in cumulative reward (p < 0.01), validating that causally guided counterfactual modeling yields substantial gains in persuasive efficacy.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We hypothesize that optimal system responses emerge from adaptive strategies grounded in causal and counterfactual knowledge. Counterfactual inference allows us to create hypothetical scenarios to examine the effects of alternative system responses. We enhance this process through causal discovery, which identifies the strategies informed by the underlying causal structure that govern system behaviors. Moreover, we consider the psychological constructs and unobservable noises that might be influencing user-system interactions as latent factors. We show that these factors can be effectively estimated. We employ causal discovery to identify strategy-level causal relationships among user and system utterances, guiding the generation of personalized counterfactual dialogues. We model the user utterance strategies as causal factors, enabling system strategies to be treated as counterfactual actions. Furthermore, we optimize policies for selecting system responses based on counterfactual data. Our results using a real-world dataset on social good demonstrate significant improvements in persuasive system outcomes, with increased cumulative rewards validating the efficacy of causal discovery in guiding personalized counterfactual inference and optimizing dialogue policies for a persuasive dialogue system.

Problem

• Infer causal and counterfactual knowledge for personalized persuasion
• Estimate latent psychological factors in user-system interactions
• Optimize dialogue policies using counterfactual data for persuasion

Innovation

• Uses causal discovery for strategy-level relationships
• Models user strategies as causal factors
• Optimizes policies via counterfactual data