CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning
The ability to perform causal and counterfactual reasoning are central properties of human intelligence. Decision-making systems that can perform these types of reasoning have the potential to be more generalizable and interpretable. Simulations have helped advance the state-of-the-art in this domain, by providing the ability to systematically vary parameters (e.g., confounders) and generate examples of the outcomes in the case of counterfactual scenarios. However, simulating complex temporal causal events in multi-agent scenarios, such as those that exist in driving and vehicle navigation, is challenging. To help address this, we present a high-fidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning in the safety-critical context. A core component of our work is to introduce \textit{agency}, such that it is simple to define and create complex scenarios using high-level definitions. The vehicles then operate with agency to complete these objectives, meaning low-level behaviors need only be controlled if necessary. We perform experiments with three state-of-the-art methods to create baselines and highlight the affordances of this environment. Finally, we highlight challenges and opportunities for future work.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal DiscoverycounterfactualCounterfactual ReasoningDecision MakingSimilar Papers 제목 키워드 기반
Robust Time Series Causal Discovery for Agent-Based Model Validation
Agent-Based Model (ABM) validation is crucial as it helps ensuring the reliability of simulations, and causal discovery has become a powerful tool in this context. However, current causal discovery methods often face acc…
Causal DiscoveryTime SeriesBivariate Causal Discovery and its Applications to Gene Expression and Imaging Data Analysis
The mainstream of research in genetics, epigenetics and imaging data analysis focuses on statistical association or exploring statistical dependence between variables. Despite their significant progresses in genetic rese…
Causal DiscoveryCausal InferenceMask2Cause: Causal Discovery via Adjacency Constrained Causal Attention
Leveraging deep learning for causal discovery in time series remains challenging because existing neural methods predominantly rely on component-wise architectures that fail to capture shared system dynamics or employ de…
Causal Foundations of Collective Agency
A key challenge for the safety of advanced AI systems is the possibility that multiple simpler agents might inadvertently form a collective agent with capabilities and goals distinct from those of any individual. More ge…
Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data
Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects. Although this ordering provides a natural scaffold for causal inference, most causal dis…
Computational EfficiencyCausal Inference