# Graphical Models for Decision-Making: Integrating Causality and Game Theory

arXiv: https://arxiv.org/abs/2504.13210 · HTML: https://mlsanity.com/paper/2504.13210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 78 references that are in the map.
- First posted on arXiv 16 Apr 2025.

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

- [A Survey on Causal Inference](https://mlsanity.com/paper/2002.02770/) (2020)
- [A Survey on Causal Discovery: Theory and Practice](https://mlsanity.com/paper/2305.10032/) (2023)
- [Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulation](https://mlsanity.com/paper/2207.12355/) (2022)
- [Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables](https://mlsanity.com/paper/2003.12659/) (2020)
- [Prior knowledge elicitation: The past, present, and future](https://mlsanity.com/paper/2112.01380/) (2021)
- [Reasoning about Causality in Games](https://mlsanity.com/paper/2301.02324/) (2023)
- [Empirical Game-Theoretic Analysis: A Survey](https://mlsanity.com/paper/2403.04018/) (2024)
- [Prospective Artificial Intelligence Approaches for Active Cyber Defence](https://mlsanity.com/paper/2104.09981/) (2021)
- [Machine learning in policy evaluation: new tools for causal inference](https://mlsanity.com/paper/1903.00402/) (2019)
- [A factorization criterion for acyclic directed mixed graphs](https://mlsanity.com/paper/1406.6764/) (2014)
- [The Impact of Variable Ordering on Bayesian Network Structure Learning](https://mlsanity.com/paper/2206.08952/) (2022)
- [Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models](https://mlsanity.com/paper/1907.11752/) (2019)
- [Causal Games and Causal Nash Equilibrium](https://mlsanity.com/paper/1910.06729/) (2019)
- [Causal Structure Learning: a Bayesian approach based on random graphs](https://mlsanity.com/paper/2010.06164/) (2020)

## Abstract (arXiv)

Causality and game theory are two influential fields that contribute significantly to decision-making in various domains. Causality defines and models causal relationships in complex policy problems, while game theory provides insights into strategic interactions among stakeholders with competing interests. Integrating these frameworks has led to significant theoretical advancements with the potential to improve decision-making processes. However, practical applications of these developments remain underexplored. To support efforts toward implementation, this paper clarifies key concepts in game theory and causality that are essential to their intersection, particularly within the context of probabilistic graphical models. By rigorously examining these concepts and illustrating them with intuitive, consistent examples, we clarify the required inputs for implementing these models, provide practitioners with insights into their application and selection across different scenarios, and reference existing research that supports their implementation. We hope this work encourages broader adoption of these models in real-world scenarios.