# Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies

arXiv: https://arxiv.org/abs/2503.16544 · HTML: https://mlsanity.com/paper/2503.16544/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [Causal Inference in the Presence of Latent Variables and Selection Bias](https://mlsanity.com/paper/1302.4983/) (2013)
- [Causal-learn: Causal Discovery in Python](https://mlsanity.com/paper/2307.16405/) (2023)
- [Deep Reinforcement Learning for Sepsis Treatment](https://mlsanity.com/paper/1711.09602/) (2017)
- [Greedy Relaxations of the Sparsest Permutation Algorithm](https://mlsanity.com/paper/2206.05421/) (2022)
- [Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation](https://mlsanity.com/paper/2012.09092/) (2020)
- [AMPERSAND: Argument Mining for PERSuAsive oNline Discussions](https://mlsanity.com/paper/2004.14677/) (2020)
- [Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies](https://mlsanity.com/paper/2001.04564/) (2020)
- [Zero-shot Persuasive Chatbots with LLM-Generated Strategies and Information Retrieval](https://mlsanity.com/paper/2407.03585/) (2024)
- [Bidirectional Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1711.07461/) (2017)
- [Counterfactual Reasoning Using Predicted Latent Personality Dimensions for Optimizing Persuasion Outcome](https://mlsanity.com/paper/2404.13792/) (2024)
- [Understanding How People Rate Their Conversations](https://mlsanity.com/paper/2206.00167/) (2022)
- [The Influence of Context on Dialogue Act Recognition](https://mlsanity.com/paper/1506.00839/) (2015)

## Built on by (most cited first, 3 of 3)

- [Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledge](https://mlsanity.com/paper/2504.13904/) (2025)
- [Enhancing Persuasive Dialogue Agents by Synthesizing Cross-Disciplinary Communication Strategies](https://mlsanity.com/paper/2602.22696/) (2026)
- [Personality-Aware Reinforcement Learning for Persuasive Dialogue with LLM-Driven Simulation](https://mlsanity.com/paper/2601.06877/) (2026)

## Abstract (arXiv)

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.