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

arXiv: https://arxiv.org/abs/2504.13904 · HTML: https://mlsanity.com/paper/2504.13904/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 84 references that are in the map.
- First posted on arXiv 8 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good](https://mlsanity.com/paper/1906.06725/) (2019)
- [Counterfactual Data Augmentation using Locally Factored Dynamics](https://mlsanity.com/paper/2007.02863/) (2020)
- [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)
- [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)
- [Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policies](https://mlsanity.com/paper/2503.16544/) (2025)
- [Advancing Counterfactual Inference through Nonlinear Quantile Regression](https://mlsanity.com/paper/2306.05751/) (2023)

## Built on by (most cited first, 2 of 2)

- [Personality-Aware Reinforcement Learning for Persuasive Dialogue with LLM-Driven Simulation](https://mlsanity.com/paper/2601.06877/) (2026)
- [CogenPVG: Cognitive-Enhanced Reflective Multi-Agent Framework for Persuasive Video Generation](https://mlsanity.com/paper/2609.25821/) (2026)

## Abstract (arXiv)

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.