Causal Reasoning over Knowledge Graphs: Can an AI Agent with Episodic Memory Perform Counterfactual Reasoning?

A Comprehensive Research Report

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1. Theoretical Foundations: Pearl’s Causal Hierarchy and Knowledge Graphs

The question of whether AI agents can reason counterfactually – asking “what would have happened if…” – sits at the apex of Judea Pearl’s Ladder of Causation, a three-level hierarchy that organizes causal reasoning by increasing sophistication:

Level 1 – Association (Seeing): Characterized by conditional probabilities P(y|x). Current deep learning systems, including most knowledge graph embedding methods, operate primarily at this level, learning statistical correlations from data.

Level 2 – Intervention (Doing): Characterized by interventional distributions P(y|do(x)). Pearl’s do-calculus provides three rules for converting interventional queries into observational quantities when certain graphical criteria (backdoor, front-door) are satisfied. This level requires a causal model – not just data.

Level 3 – Counterfactual (Imagining): Characterized by retrospective queries of the form “Given that Y happened, what would Y have been if X had been different?” This requires a full Structural Causal Model (SCM) with specific functional relationships, not merely a causal graph. Pearl has argued that model-free approaches (including standard reinforcement learning) fundamentally cannot compute counterfactuals, as counterfactuals by definition require models.

The central thesis emerging from recent literature is that knowledge graphs, when augmented with causal structure and episodic memory, can serve as the substrate for agents to ascend this ladder – but significant gaps remain between current capabilities and true counterfactual reasoning.

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2. Causal Knowledge Graphs: From Correlation to Causation

2.1 CausalKG: The Foundational Framework

The seminal CausalKG framework (Jaimini & Sheth, 2022) established the paradigm of enriching knowledge graphs with explicit causal semantics to support interventional and counterfactual reasoning. The key insight was that existing knowledge graphs represent causal relationships extracted from text based on linguistic patterns, but this representation is insufficient for counterfactual reasoning. CausalKG addresses this by:

• Using hyper-relational graph representations to encode complex causal relations
• Leveraging domain knowledge embedded in the KG to provide a comprehensive search space for possible interventional and counterfactual variables
• Exploiting KG expressivity to generate human-understandable explanations

CausalKG demonstrated that the fusion of causal models with knowledge graphs is not merely theoretical – it enables practical AI explainability through causal reasoning grounded in domain knowledge.

2.2 CausE: Causal Knowledge Graph Embeddings

The CausE framework (Zhang & Zhang, CCKS 2023) brought causal inference directly into knowledge graph embedding. Standard KGE methods learn correlations that can be misled by trivial patterns and noisy links. CausE employs causal intervention to estimate the causal effect of confounder embeddings and designs new training objectives for stable predictions. Extensions in 2024 include CausalLP (weighted causal link prediction) and HyperCausalLP (hyper-relational causal link prediction), indicating a maturing subfield.

2.3 Automated Construction with LLMs (2024-2025)

The construction of causal knowledge graphs has been transformed by LLMs. Key developments include:

• Ontogenia (2025): Metacognitive prompting for ontology generation with self-reflection and structural correction
• CQbyCQ (2024): Direct translation of requirements into OWL-compliant schemas
• EDC Framework (2024): Three-stage Extract-Define-Canonicalize pipeline for automated schema induction
• CausalKGPT (2024): Industrial causal knowledge-enhanced LLM for manufacturing quality analysis

The Structured Knowledge-based Causal Discovery approach (Le, Xia & Chen, 2025, Information Processing & Management) introduced agentic “streams of thought” where multiple LLM agents collaborate on causal discovery – a Meta Agents Model handles reasoning while a Coding Agents Model handles data analysis, combining structured knowledge with statistical methods.

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3. Do-Calculus Applied to Knowledge Graphs

3.1 From Theory to Implementation

Pearl’s three rules of do-calculus provide the mathematical machinery for converting interventional queries into estimable quantities from observational data, given a causal DAG. Recent work has operationalized this within graph-based systems:

• CLIP (Causal LInk Predictor): Applies backdoor adjustment via do-calculus to remove structural bias in temporal link prediction, enabling inductive generalization to unseen entities.
• Causal GNN Integration (Job, 2025 review in WIREs): Methods such as structural equation modeling, causal graph learning, and do-calculus-based interventions can be integrated into Graph Neural Networks, enabling them to move from correlation to causation.
• Causal Reinforcement Learning: Formalizes model-based RL as a causal inference problem using do-calculus, enabling agents to reason about the effects of interventions in a “counterfactual dream world” where hypothetical actions are simulated via do-interventions.

3.2 Causal Inference for Knowledge Graph Completion

A line of work at the intersection of causal inference and KG completion argues that because the world is driven by causality rather than correlation, purely correlation-driven KGC models suffer from data bias and lack interpretability. Causal KGC models leverage causal graphs for intuitive interpretation and intervention techniques for controllability. CausalDisco (2024) inverts this relationship entirely, formulating causal discovery itself as a knowledge graph completion problem – using KG link prediction methods to discover missing causal edges.

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4. Counterfactual Reasoning Models on Graphs

4.1 The Survey Landscape

The comprehensive survey “Counterfactual Learning on Graphs” (Guo et al., Machine Intelligence Research, 2025) provides the most systematic treatment of this space. It categorizes methods into four problem areas: counterfactual fairness, explainability, link prediction, and other applications. Key methods include:

• CF-GNNExplainer: Generates counterfactual explanations via minimal perturbation to input graphs (edge deletions) that change predictions
• CI-GNN: Extracts causal functionality connections using GraphVAE for disentangled latent representations
• Causal Concept Graph Models (ICLR 2025): Post-hoc explainability using surrogate causal models (DiConStruct, conceptual counterfactual explanations)

4.2 Counterfactual Reasoning with Knowledge Graphs for NLP

The EACL 2024 paper on “Counterfactual Reasoning with Knowledge Graphs” demonstrates that KG-grounded counterfactual reasoning improves performance on downstream NLP tasks. The approach generates counterfactual scenarios by manipulating entities and relations within the KG, then evaluating how changes propagate through the graph structure. This directly operationalizes “what would have happened if…” queries against structured knowledge.

4.3 Benchmarking LLM Counterfactual Capabilities

CausalProbe 2024 (NeurIPS 2024) revealed that current LLMs struggle with genuine causal reasoning. Even Claude 3 Opus achieved less than 70% exact match on the hard counterfactual reasoning benchmark. The COLD framework (NeurIPS 2024) and CausalBench further probe causal understanding from four perspectives: cause-to-effect, effect-to-cause, and both with intervention. These benchmarks consistently show that LLMs may recall embedded causal knowledge from training data rather than performing true causal inference.

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5. Combining Causal AI with Memory Systems (2024-2026)

This is the most rapidly evolving area, where the question posed – “can an AI agent with episodic memory perform counterfactual reasoning?” – is being directly addressed.

5.1 Cognitive Science Foundation

A pivotal 2025 paper, “The role of episodic memory in causal reasoning with counterfactuals” (Rappe, Philosophical Psychology, accepted April 2025), provides the theoretical foundation. The paper argues that episodic memory yields adaptive success specifically because of its role in singular counterfactual causal reasoning. The proposed three-step model:

1. Generate an episodic memory related to the potential cause
2. Construct a counterfactual scenario through inhibition of the relevant part of the past episode
3. Temporal evolution followed by alternative model evaluation

This translates David Lewis’s counterfactual model of causation into the predictive processing framework, grounding it in trace minimalism. The hippocampus – the brain region most associated with episodic memory – has been identified in fMRI studies as integral to counterfactual reasoning, though the precise mechanism remains under investigation.

A complementary 2026 paper, “Causal and noncausal contributions to episodic memory: a computational perspective” (Philosophical Psychology), further analyzes scenario construction as a common process where episodic recall constructs scenarios from memory traces and semantic information, while imagination constructs counterfactual scenarios from semantic information and multiple episodic traces.

5.2 The Concept of Autobiographical Causality

The concept of autobiographical causality (Curry, 2025) proposes that sophisticated causal reasoning requires episodic memory as its substrate. Unlike statistical causal inference from datasets, autobiographical causality is derived from personally experienced, temporally ordered, emotionally salient episodic memories. The central thesis: you cannot truly understand causality without remembering your lived experiences of cause and effect. For AI, this means agents with episodic memory can analogize from their own “lived” experiences – a step toward artificial general intelligence.

5.3 Agent Memory Architectures with Causal Components

MAGMA (January 2026) – The Multi-Graph Agentic Memory Architecture (Jiang et al., UT Dallas/U. Florida) represents each memory item across four orthogonal relational graphs:
- Temporal Graph: Strictly ordered chronological pairs
- Causal Graph: Directed edges representing logical entailment, inferred by an LLM-based consolidation module
- Semantic Graph: Undirected edges connecting conceptually similar events
- Entity Graph: Connects events to abstract entity nodes for object permanence

MAGMA achieved 0.700 overall score on LoCoMo (outperforming baselines by 18.6-45.5%), 61.2% on LongMemEval while reducing token consumption by 95%, and 1.47-second query latency. The causal graph component specifically enables “Why” queries by traversing why-because relationships rather than semantic associations.

Zep/Graphiti (January 2025) – A temporal knowledge graph architecture for agent memory with a bi-temporal model tracking when events occurred and when they were ingested. The Episode Subgraph records episodic memory with raw events annotated with timestamps, achieving 94.8% on the DMR benchmark at P95 latency of 300ms.

Hindsight (December 2025) – Organizes agent memory into four logical networks: world network (objective facts), bank network (agent experiences in first person), opinion network (subjective judgments with confidence scores), and observation network (preference-neutral entity summaries). Improved accuracy by +44.6 points over full-context baselines. While not explicitly causal, the separation of evidence from inference and the reflect operation that updates beliefs based on new evidence creates a substrate for causal updating.

MemRL (January 2026) – Enables agents to self-evolve via non-parametric reinforcement learning on episodic memory, implementing Constructive Episodic Simulation – retrieving past experiences to synthesize solutions for novel tasks. Uses Two-Phase Retrieval: filtering by semantic relevance, then selecting by learned Q-values (utility). Outperforms baselines on HLE, BigCodeBench, ALFWorld, and Lifelong Agent Bench.

5.4 The “Memory in the Age of AI Agents” Survey (December 2025)

This comprehensive survey proposes a three-dimensional taxonomy:
- Forms: Token-level, parametric, and latent memory
- Functions: Factual memory (declarative databases), experiential memory (procedural knowledge, case libraries, strategic templates), and working memory
- Dynamics: How memory is formed, evolved, and retrieved over time

The survey maps these to cognitive neuroscience concepts (episodic vs. semantic memory) and identifies that episodic memory in agents captures procedural history including intermediate steps, tool call sequences, and decision branches – precisely the information needed for counterfactual replay.

5.5 The Most Recent Survey (March 2026)

“Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers” (March 2026) formalizes agent memory as a write-manage-read loop and identifies five mechanism families: context-resident compression, retrieval-augmented stores, reflective self-improvement, hierarchical virtual context, and policy-learned management. It explicitly names causally grounded retrieval as an open challenge for the field.

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6. Causal-Counterfactual RAG: The Integration Point

Three 2025 frameworks directly address counterfactual reasoning in retrieval-augmented systems:

6.1 CausalRAG (ACL Findings 2025)

Wang et al. construct causal graphs by parsing documents with LLMs to identify entities as nodes and infer causal relationships as edges. At query time, top-k nodes are matched via embedding distance, expanded along graph edges, and an LLM identifies causal paths within retrieved nodes. Results: Context Precision 92.86 (vs. 71.01 for standard RAG), Answer Faithfulness 78.00 (vs. 61.15).

6.2 Causal-Counterfactual RAG (September 2025)

Khadilkar & Gupta build a Causal Knowledge Graph using Gemini 1.5 to extract (cause, effect) pairs with 384-dimensional SentenceTransformer embeddings and two-stage verification. The key innovation is counterfactual simulation: the system generates logical opposites of identified causes, encodes them, and traverses the graph to simulate alternate outcomes. An arbitration mechanism compiles factual causal chains and counterfactual simulation outcomes into evidence packages. Results: Precision 80.57, Recall 78.18, Causal Chain Integrity 75.58, Counterfactual Robustness 69.90.

6.3 Counterfactual RAG (CF-RAG)

Addresses the “Correlation Trap” where existing systems cannot distinguish causally decisive evidence from overwhelmingly correlated yet misleading information. Systematically generates and evaluates counterfactual queries using a parallel arbitration mechanism to reconcile conflicting evidence.

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7. Embodied Causal Agents: ADAM and Policy4OOD

7.1 ADAM (ICLR 2025)

The ADAM agent (Yu & Lu) demonstrates that an embodied agent can autonomously construct a causal world model from scratch in open-world environments (modified Minecraft). Its four modules – interaction, causal model, controller (with memory pool), and perception – enable it to build a near-perfect causal graph without prior knowledge, achieving efficient task decomposition with strong interpretability and human-like exploration behavior.

7.2 Policy4OOD (February 2026)

A knowledge-guided world model that converts policy documents into a policy knowledge graph using LLMs, encodes them via relational GNNs, and uses vector-quantized codebooks to discover canonical intervention strategies. The system unifies forecasting, counterfactual reasoning about alternative past decisions, and optimization over candidate interventions through a Transformer with dual-branch training (masked token prediction + next-token prediction for causal dynamics).

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8. Assessment: Can AI Agents with Episodic Memory Perform Counterfactual Reasoning?

What is Currently Possible

Yes, with significant caveats. The evidence shows:

1. Agents can construct causal graphs from experience. ADAM demonstrates autonomous causal graph construction in open-world environments. LLM-based systems can extract causal structures from text. Temporal KG architectures (Zep/Graphiti, MAGMA) can maintain evolving causal representations.

2. Agents can perform approximate counterfactual simulation. Causal-Counterfactual RAG demonstrates generating counterfactual causes and simulating alternate outcomes through graph traversal. Policy4OOD performs counterfactual policy analysis. Causal RL agents can simulate interventions in learned world models.

3. Episodic memory provides the necessary substrate. MAGMA’s four-graph architecture (including a causal graph) stores the temporal, causal, and semantic structure needed for counterfactual queries. MemRL’s Constructive Episodic Simulation retrieves past experiences to synthesize solutions for novel tasks. Cognitive science research confirms that episodic memory is essential for singular counterfactual causal reasoning.

What Remains Challenging

1. True Rung-3 counterfactuals require full SCMs. Current systems largely perform Rung-2 (interventional) reasoning or approximate Rung-3 reasoning via LLM-based simulation rather than formal counterfactual inference over structural equations. The gap between “what would an LLM predict as a counterfactual” and “what does the SCM entail as a counterfactual” remains significant.

2. LLM causal reasoning has fundamental limitations. CausalProbe 2024 shows that even frontier LLMs struggle with genuine counterfactual reasoning, often recalling memorized causal knowledge rather than performing true inference. Critics argue LLMs may confuse correlation with causation.

3. Scalability and consistency. Constructing and maintaining large causal knowledge graphs that are simultaneously complete, consistent, and causally valid remains an open engineering challenge. Causally grounded retrieval is explicitly identified as an unsolved problem in the March 2026 memory survey.

4. Integration gap. While the components exist – episodic memory architectures, causal graph construction, counterfactual simulation, do-calculus reasoning – no single system yet integrates all of them into a unified agent that can: (a) accumulate episodic experiences, (b) extract causal structure from those experiences, (c) perform formal counterfactual inference using that structure, and (d) use the results to improve future decisions.

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9. Key Open Research Directions

1. Causally grounded episodic retrieval: Moving from semantic similarity to causal relevance as the primary retrieval criterion for agent memory
2. Formal counterfactual inference in neural systems: Bridging the gap between LLM-approximate and SCM-formal counterfactual reasoning
3. Continual causal model refinement: Agents that update their causal graphs as they accumulate experience, handling contradictions and model revision
4. Trustworthy reflection: Ensuring that agent self-evaluation and belief updating are grounded in valid causal reasoning rather than confabulation
5. Multimodal embodied causal memory: Extending causal episodic memory beyond text to sensory and motor experiences

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Sources

• CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning – Jaimini & Sheth, IEEE Internet Computing, 2022
• CausE: Towards Causal Knowledge Graph Embedding – Zhang & Zhang, CCKS 2023
• Counterfactual Reasoning with Knowledge Graphs – EACL 2024
• Counterfactual Learning on Graphs: A Survey – Machine Intelligence Research, 2025
• Exploring Causal Learning Through Graph Neural Networks: An In-Depth Review – Job, WIREs Data Mining and Knowledge Discovery, 2025
• Causal Inference Meets Deep Learning: A Comprehensive Survey – PMC, 2024
• CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation – ACL Findings 2025
• Causal-Counterfactual RAG: The Integration of Causal-Counterfactual Reasoning into RAG – Khadilkar & Gupta, September 2025
• Counterfactual Reasoning for Retrieval-Augmented Generation (CF-RAG) – OpenReview, 2025
• The Role of Episodic Memory in Causal Reasoning with Counterfactuals – Rappe, Philosophical Psychology, 2025
• Causal and Noncausal Contributions to Episodic Memory: A Computational Perspective – Philosophical Psychology, 2026
• ADAM: An Embodied Causal Agent in Open-World Environments – Yu & Lu, ICLR 2025
• MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents – Jiang et al., January 2026
• Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects – Latimer et al., December 2025
• MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory – January 2026
• Memory in the Age of AI Agents: A Survey – December 2025
• Memory for Autonomous LLM Agents: Mechanisms, Evaluation, and Emerging Frontiers – March 2026
• Zep: A Temporal Knowledge Graph Architecture for Agent Memory – Rasmussen, January 2025
• Unveiling Causal Reasoning in Large Language Models: Reality or Mirage? – NeurIPS 2024
• COLD: Causal Reasoning in Closed Daily Activities – NeurIPS 2024
• Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation – February 2026
• Structured Knowledge-based Causal Discovery: Agentic Streams of Thought – Le, Xia & Chen, Information Processing & Management, 2025
• Counterfactual Causal Inference in Natural Language with Large Language Models – 2024
• Large Causal Models from Large Language Models – December 2025
• CausalDisco: Causal Discovery Using Knowledge Graph Link Prediction – 2024
• Causal Concept Graph Models (ICLR 2025) – ICLR 2025
• Causal Reinforcement Learning – Bareinboim, CausalAI Lab
• Beyond Context Graphs: Why 2026 Must Be the Year of Agentic Memory, Causality, and Explainability – Pavlyshyn, 2026
• Causal Inference: A Tale of Three Frameworks – 2025
• From Probability to Counterfactuals: Increasing Complexity in Pearl’s Causal Hierarchy – 2024
• Autobiographical Causality: How Episodic Memory Enables Lived Causal Understanding in AI – Curry, November 2025
• Graphiti: Knowledge Graph Memory for an Agentic World – Neo4j/Zep
• Causal Inference for Knowledge Graph Completion – OpenReview
• Knowledge Graphs as Unified Agentic Memory for Improved Retrieval, Reasoning and Causal Analysis – Springer, 2025/2026