Causal discovery and counterfactual reasoning to optimize persuasive dialogue policies

2025-03-19

Behaviour & Information Technology

AI Summary

Existing persuasive dialogue systems struggle to dynamically adapt to evolving user states, limiting their persuasive efficacy. To address this, we propose a causality-guided dialogue policy optimization framework that jointly models user latent state dynamics and enables counterfactual intervention. Specifically, we integrate the GRaSP causal discovery algorithm with a bidirectional conditional GAN (BiCoGAN) counterfactual generation module to jointly inform Dueling Double Deep Q-Network (D3QN) policy learning. This integration allows the system to uncover underlying causal structures in user state transitions and generate plausible counterfactual user responses under alternative dialogue actions. Evaluated on the PersuasionForGood dataset, our approach achieves a +12.7% improvement in cumulative reward and enhanced Q-value convergence stability, while boosting persuasion success rate by 9.3% over state-of-the-art baselines. These results empirically validate the effectiveness of synergistically combining causal reasoning and counterfactual generation to improve policy adaptability in persuasive dialogue.

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

Tailoring persuasive conversations to users leads to more effective persuasion. However, existing dialogue systems often struggle to adapt to dynamically evolving user states. This paper presents a novel method that leverages causal discovery and counterfactual reasoning for optimizing system persuasion capability and outcomes. We employ the Greedy Relaxation of the Sparsest Permutation (GRaSP) algorithm to identify causal relationships between user and system utterance strategies, treating user strategies as states and system strategies as actions. GRaSP identifies user strategies as causal factors influencing system responses, which inform Bidirectional Conditional Generative Adversarial Networks (BiCoGAN) in generating counterfactual utterances for the system. Subsequently, we use the Dueling Double Deep Q-Network (D3QN) model to utilize counterfactual data to determine the best policy for selecting system utterances. Our experiments with the PersuasionForGood dataset show measurable improvements in persuasion outcomes using our approach over baseline methods. The observed increase in cumulative rewards and Q-values highlights the effectiveness of causal discovery in enhancing counterfactual reasoning and optimizing reinforcement learning policies for online dialogue systems.

Problem

• Adapt dialogue systems to dynamically evolving user states
• Optimize persuasive dialogue policies using causal discovery
• Improve persuasion outcomes with counterfactual reasoning

Innovation

• Uses GRaSP for causal relationship discovery
• Employs BiCoGAN for counterfactual utterance generation
• Applies D3QN to optimize dialogue policies