# CausalKG: Causal Knowledge Graph Explainability using interventional and counterfactual reasoning

arXiv: https://arxiv.org/abs/2201.03647 · HTML: https://mlsanity.com/paper/2201.03647/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 6 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach](https://mlsanity.com/paper/2003.07703/) (2020)
- [Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving](https://mlsanity.com/paper/1811.11277/) (2018)

## Built on by (most cited first, 14 of 14)

- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [CausalLP: Learning causal relations with weighted knowledge graph link prediction](https://mlsanity.com/paper/2405.02327/) (2024)
- [Causal Inference with Latent Variables: Recent Advances and Future Prospectives](https://mlsanity.com/paper/2406.13966/) (2024)
- [Deconstructing Depression Stigma: Integrating AI-driven Data Collection and Analysis with Causal Knowledge Graphs](https://mlsanity.com/paper/2502.06075/) (2025)
- [Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts](https://mlsanity.com/paper/2504.07459/) (2025)
- [causalgraph: A Python Package for Modeling, Persisting and Visualizing Causal Graphs Embedded in Knowledge Graphs](https://mlsanity.com/paper/2301.08490/) (2023)
- [Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs](https://mlsanity.com/paper/2309.16593/) (2023)
- [Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI](https://mlsanity.com/paper/2411.03225/) (2024)
- [ClimateCause: Complex and Implicit Causal Structures in Climate Reports](https://mlsanity.com/paper/2604.14856/) (2026)
- [WikiCausal: Corpus and Evaluation Framework for Causal Knowledge Graph Construction](https://mlsanity.com/paper/2409.00331/) (2024)
- [Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda](https://mlsanity.com/paper/2501.13763/) (2025)
- [Linked Data Classification using Neurochaos Learning](https://mlsanity.com/paper/2602.16204/) (2026)
- [AGEL-Comp: A Neuro-Symbolic Framework for Compositional Generalization in Interactive Agents](https://mlsanity.com/paper/2604.26522/) (2026)
- [Implicit Causal Graph Construction in Text via Chain Discovery](https://mlsanity.com/paper/2606.07525/) (2026)

## Abstract (arXiv)

Humans use causality and hypothetical retrospection in their daily decision-making, planning, and understanding of life events. The human mind, while retrospecting a given situation, think about questions such as "What was the cause of the given situation?", "What would be the effect of my action?", or "Which action led to this effect?". It develops a causal model of the world, which learns with fewer data points, makes inferences, and contemplates counterfactual scenarios. The unseen, unknown, scenarios are known as counterfactuals. AI algorithms use a representation based on knowledge graphs (KG) to represent the concepts of time, space, and facts. A KG is a graphical data model which captures the semantic relationships between entities such as events, objects, or concepts. The existing KGs represent causal relationships extracted from texts based on linguistic patterns of noun phrases for causes and effects as in ConceptNet and WordNet. The current causality representation in KGs makes it challenging to support counterfactual reasoning. A richer representation of causality in AI systems using a KG-based approach is needed for better explainability, and support for intervention and counterfactuals reasoning, leading to improved understanding of AI systems by humans. The causality representation requires a higher representation framework to define the context, the causal information, and the causal effects. The proposed Causal Knowledge Graph (CausalKG) framework, leverages recent progress of causality and KG towards explainability. CausalKG intends to address the lack of a domain adaptable causal model and represent the complex causal relations using the hyper-relational graph representation in the KG. We show that the CausalKG's interventional and counterfactual reasoning can be used by the AI system for the domain explainability.