# Characterising Interventions in Causal Games

arXiv: https://arxiv.org/abs/2406.09318 · HTML: https://mlsanity.com/paper/2406.09318/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective](https://mlsanity.com/paper/1908.04734/) (2019)
- [Agent Incentives: A Causal Perspective](https://mlsanity.com/paper/2102.01685/) (2021)
- [Characterizing Manipulation from AI Systems](https://mlsanity.com/paper/2303.09387/) (2023)
- [Discovering Agents](https://mlsanity.com/paper/2208.08345/) (2022)
- [Path-Specific Objectives for Safer Agent Incentives](https://mlsanity.com/paper/2204.10018/) (2022)
- [Reasoning about Causality in Games](https://mlsanity.com/paper/2301.02324/) (2023)
- [Why Fair Labels Can Yield Unfair Predictions: Graphical Conditions for Introduced Unfairness](https://mlsanity.com/paper/2202.10816/) (2022)
- [Identifying Independencies in Causal Graphs with Feedback](https://mlsanity.com/paper/1302.3595/) (2013)
- [Robust Multi-agent Counterfactual Prediction](https://mlsanity.com/paper/1904.02235/) (2019)
- [On Imperfect Recall in Multi-Agent Influence Diagrams](https://mlsanity.com/paper/2307.05059/) (2023)

## Abstract (arXiv)

Causal games are probabilistic graphical models that enable causal queries to be answered in multi-agent settings. They extend causal Bayesian networks by specifying decision and utility variables to represent the agents' degrees of freedom and objectives. In multi-agent settings, whether each agent decides on their policy before or after knowing the causal intervention is important as this affects whether they can respond to the intervention by adapting their policy. Consequently, previous work in causal games imposed chronological constraints on permissible interventions. We relax this by outlining a sound and complete set of primitive causal interventions so the effect of any arbitrarily complex interventional query can be studied in multi-agent settings. We also demonstrate applications to the design of safe AI systems by considering causal mechanism design and commitment.