  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.
