From Plausible AI to Explainable AI

Climbing the Enterprise AI Intelligence Ladder: From Stochastic Correlation and Reasoning to Structured Causality

Figure 1: Word Cloud of Relevant Concepts and Factors for Causality

As AI agents encounter a hard “trust wall,” the next frontier of enterprise AI is about mature reasoning and structured causality that is grounded in first-principles thinking. A deep dive into the journey from the misleading fluency of the “Causal Parrot” to the auditable, defensible power of Causal AI Decision Intelligence is a non-negotiable pre-requisite for ‘Responsible AI’ solutions.

In just a short span of a few years, the AI paradigm has progressed from the statistical forecasting of predictive analytics to the remarkable fluency of generative AI and now stands at the dawn of the agentic era. AI agents, powered by Large Language Models (LLMs), are beginning to automate complex, multi-step workflows, promising a new era of operational efficiency, productivity, pro-activity (and in future preventability!). Yet, as these agents are tasked with moving beyond rote automation to making and influencing high-stakes business decisions, a stark and unavoidable reality is setting in. The dominant technical stack consisting of LLMs, Chain-of-Thought (CoT) prompting, and Retrieval-Augmented Generation (RAG) is encountering a hard, unyielding trust wall.

For any enterprise operating in a world of regulatory compliance, financial accountability, and legal liability, this is an untenable risk. An explanation that sounds right is not the same as a decision you can defend. This growing tension is why, as new market analysis from theCUBE Research compellingly argues, the industry is poised for its next great pivot. They predict that 2026 will be the breakout year for Causal AI Decision Intelligence: a new, essential layer in the AI stack designed to move beyond superficial correlation and imbue AI with a genuine, mechanistic understanding of cause and effect. The demand for this shift is palpable. TheCUBE’s Agentic AI Futures Index shows that while only 46% of enterprise leaders currently trust AI agents to make and justify decisions, a full 62% plan to evolve their AI strategy beyond simple automation towards this more robust Decision Intelligence within the next 18 months.

Achieving this requires a deliberate ascent up what can be described as the “Intelligence Ladder,” a methodological journey from the misleading shallows of correlation to the solid ground of causality. This article explores that journey in depth, beginning with the foundational problem that necessitates it: the rise of the “Causal Parrot.”

The “Causal Parrot” Problem: When Fluency Masks a Void of Understanding

Before we can appreciate the solution, we must first dissect the problem. The “Causal Parrot” is a term that captures the essential weakness of today’s dominant AI models when faced with consequential decisions. A Causal Parrot is a system that can fluently and confidently repeat statistical correlations it has learned from its training data, presenting them as if they are verified causal relationships, all without possessing any underlying model of how the real world works.

Why Does This Happen?

1. Observational (Static) Training Data: LMs are trained on a vast, static snapshot of the internet, i.e. a repository of text and images. They learn statistical associations from this observational data. For example, an LLM learns that the phrases “database server” and “latency issues” frequently appear in the same technical documents. It learns a strong correlation. However, it has never been trained on interventional data. It has never seen a dataset showing what happens when a database administrator doubles the server’s memory and observes the resulting change in latency. Its knowledge is passive, not active.
2. Next-Token Prediction vs. World Modelling: At their core, LLMs are next-token predictors. Their fundamental objective is to determine the most statistically probable next word in a sequence to create coherent, human-like language. They are optimized for linguistic plausibility, not factual accuracy or causal reasoning. They do not build an internal “world model” with objects, relationships, and causal mechanisms.

As a trained data scientist, the cliched ice-cream-and-drownings example is what comes to mind to demonstrate this failure mode in a more obvious way which exposes the classic ‘correlation doesn’t imply causation’ dilemma. When data consistently shows a strong positive correlation between ice cream sales and the number of drowning incidents. A purely correlational AI, when asked how to reduce drowning deaths, might plausibly suggest a campaign to reduce ice cream consumption. The recommendation is logically consistent with the data pattern, but it is causally absurd. The AI has failed to identify the hidden confounder, i.e. the summer heat that independently causes both phenomena

Figure 2 : Correlation Causation Meme Sourced From XKCD

In enterprise contexts, the same flaw appears in far more expensive and subtle ways: mistaking symptoms for causes, correlations for levers, and narratives for mechanisms. An LLM-based agent might notice that top-performing sales teams spend more on client entertainment. Asked how to improve a struggling team, it might confidently recommend increasing their entertainment budget. It sounds plausible. However, it may have missed the true causal driver: the top teams are assigned to higher-value territories, which both generates more revenue and justifies a larger entertainment budget. The budget is a symptom of their success, not the cause of it. Acting on the AI’s recommendation would waste money without addressing the root issue of territory allocation.

As Joel Sherlock, CEO of Causify.ai, notes:

“Prediction isn’t decision-making. Real decisions require an understanding of why, and what changes if you act differently.”

The Toolkit of Machine Cognition: A Deeper Survey of Reasoning in AI

To truly grasp how an AI system climbs the Intelligence Ladder, one must first understand the tools it uses for the ascent. These are based on the foundational first principles of reasoning in causal models across different disciplines from axiomatic mathematical systems to scientific and Bayesian probability based approaches. The following are the core reasoning methods that form this cognitive toolkit.

1. Deductive Reasoning: The Logic of Certainty

Description: Deductive reasoning is the most formal and rigorous mode of thought. It operates “top-down,” starting from a set of general, accepted premises or rules and applying them to a specific case to arrive at a conclusion that is logically certain. If the initial premises are true, the conclusion must be true.

Application in AI: This is the bedrock of early “expert systems” and is critical in modern AI governance and policy enforcement agents. An AI agent overseeing a CI/CD pipeline might use deductive reasoning to automatically block a deployment that lacks the required security scan approval, ensuring compliance with no room for ambiguity.

2. Inductive Reasoning: The Logic of Probability

Description: Inductive reasoning works in the opposite direction. It is a “bottom-up” process that observes numerous specific examples to form a broad, general conclusion. This conclusion is not a certainty but a high-probability inference.

Application in AI: This is the engine of modern machine learning and underpins enterprise customer-intelligence platforms that ingest large volumes of unstructured feedback, detect recurring patterns, and surface the foundational “what is happening” insights that trigger deeper investigation.

3. Abductive Reasoning: The Logic of Plausible Inference

Description: Where induction identifies a pattern, abduction seeks to explain it. Often described as “inference to the best explanation,” it is the process of generating the simplest and most plausible hypothesis for a given set of (often incomplete) observations.

Application in AI: This mode is central to incident orchestration and diagnostic systems that synthesise multiple clues like changes, dependencies, symptoms, and prior patterns into the most plausible causal hypothesis to guide investigation.

4. Analogical Reasoning: The Logic of Precedent

Description: This involves solving a new problem by identifying a structurally similar problem from the past and adapting its solution. It is about transferring knowledge from a familiar source domain to a novel target domain.

Application in AI: This reasoning is vital in historical incident retrieval and precedent-based support systems that identify structurally similar prior failures, resolutions, or customer scenarios rather than relying on superficial keyword overlap alone.

5. Temporal Reasoning: The Logic of Time

Description: This is a specialised form of reasoning that deals explicitly with time, sequence, duration, and concurrency. It uses formalisms (like Allen’s Interval Algebra) to understand relationships like before, after, during, overlaps, and meets.

Application in AI: Temporal reasoning is essential in systems that must correlate changes, behaviours, and outcomes over time from trend analysis in customer experience to identifying whether a deployment, configuration change, or dependency failure preceded an operational incident.

6. Common Sense Reasoning: The Logic of Unspoken/’Undocumented’ Knowledge

Description: This remains one of AI’s most ambitious frontiers. It involves imbuing a system with the vast body of implicit, everyday knowledge that humans use to understand the world.

Application in AI: This capability is critical in natural-language enterprise interfaces, where users ask ambiguous, figurative, or shorthand questions and expect the system to infer the intended operational meaning rather than merely parse literal wording.

7. Monotonic vs. Non-monotonic Reasoning: The Logic of Adaptability

Description: This distinction defines how an AI system handles new information.

Monotonic: The knowledge base only grows. Once a fact is asserted, it can never be retracted.

Non-monotonic: Conclusions are provisional and can be retracted when new, contradictory evidence emerges. This is how humans reason in the real world.

Application in AI: Non-monotonic reasoning is essential in diagnostic, investigative, and autonomous systems that must reject an initial hypothesis when later evidence fails to support it and pivot toward a better explanation instead of persisting with false certainty.

8. Fuzzy Reasoning: The Logic of Vagueness

Description: Traditional logic is binary (true/false, 1/0). Fuzzy reasoning handles the inherent vagueness of the real world by allowing for degrees of truth.

Application in AI: This is particularly useful in sentiment, risk, and control-oriented systems, where categories are rarely clean-cut and nuanced judgments such as “slightly negative,” “moderately risky,” or “mostly stable” carry more operational value than hard binaries.

Causality in AI: Moving Beyond ‘What’ to ‘Why’

While the reasoning methods above are powerful tools for analysis, they primarily operate on observational data. Causal AI represents a paradigm shift. It introduces a new set of tools and a mathematical framework for moving beyond correlation to understand the underlying cause-and-effect mechanisms of a system. Its goal is not just to describe the world as it is, but to predict how the world would change if we intervened. This is achieved through three core concepts.

1. Structural Causal Models (SCMs): Mapping the Mechanisms

Concept: An SCM is a mathematical representation of the “data-generating process” — the underlying rules that govern a system. It is often visualised as a Directed Acyclic Graph (DAG), where nodes represent variables and arrows represent direct causal relationships. For example, in an e-commerce system, an arrow might go from Database Latency to Page Load Time, indicating that the former directly influences the latter.

Application: In advanced operational architectures, service dependency maps, architecture diagrams, and event relationships can begin to approximate an SCM. A mature Causal AI implementation would formalise this into a dynamic, query-able graph, providing the causal scaffolding for investigation and intervention.

2. Interventions (The do-operator): Asking “What if we act?”

Concept: This is the cornerstone of Causal AI, formalised by Judea Pearl’s do-calculus. It distinguishes between seeing and doing. Seeing means filtering the data — for example, looking only at data points where page load time is high. Doing means simulating a change to the system’s mechanics — for example, asking what would happen if database latency were forcibly set to 10ms. This allows an AI system to reason about the effects of actions it has not yet observed directly.

Application: In production settings, intervention logic appears whenever a system recommends an action such as rolling back a deployment, scaling resources, or changing a workflow under the assumption that doing so will alter outcomes in a predictable direction.

3. Counterfactuals: Asking “What if things had been different?”

Concept: Counterfactuals are the most sophisticated level of causal reasoning. They involve reasoning about a hypothetical past. While an intervention asks, “What will happen if we do X?”, a counterfactual asks, “Given that Y happened, what would have happened if we had done X instead?”

Application: This is crucial for root-cause analysis and organisational learning. After an outage is resolved, a team might ask whether the incident would still have occurred had a different configuration, release path, or resource profile been in place. This is the level of reasoning human experts apply instinctively, and which advanced causal systems aim to support systematically.

A Tale of Two Systems: A Practical Journey Up the Intelligence Ladder

The conceptual ascent from “what” to “why” becomes tangible and actionable when we analyse two distinct but complementary examples of real-world AI systems. They represent two essential stages of organisational maturity, providing a clear roadmap for any enterprise looking to build truly intelligent capabilities. Before we dig deeper, here is the overview of the two systems designed for two distinct projects and clients but with shared learnings on information triaging:

System (Project) 1: AI-Led Insights and On-Demand Analytics for Total Quality Management

System (Project) 2: Multi-Agent System to Manage Major Incident Management Operations

Stage 1: Mastering Correlation and Its Nuanced Failures with a Customer Intelligence Platform

The first system represents a foundational capability that has become table stakes for any data-driven B2C organisation: an AI-powered platform for analysing customer sentiment and feedback.

Its Mandate and Objective: This platform’s primary mission is to provide a single, unified source of truth about the voice of the customer. It is engineered to ingest a firehose of unstructured data from dozens of public and private channels — social media platforms, app store reviews, e-commerce feedback, community forums, and customer support transcripts. Its objective is to synthesise this qualitative chaos into a quantitative, interactive dashboard that allows business leaders to understand trends, benchmark performance, and identify emerging issues before they escalate into crises. It is designed to answer the question: “What are our customers saying, where are they saying it, and how is it changing over time?”

Architecture and Reasoning in Action: The platform’s intelligence is built on a sophisticated inductive reasoning pipeline. As raw text data flows in, a series of specialised NLP models classify each post by topic — such as “Build Quality” or “User Interface” — and sentiment: positive, neutral, or negative. These individual classifications are then aggregated into statistically meaningful metrics. The dashboard visualises these correlations, showing, for example, that “Sales Process” garners a 94% positive rating while “Aftersales Support” drops to a worrying 66%. Temporal reasoning adds the dimension of time, revealing on a line chart that sentiment for a flagship product, stable for months, began a sharp downturn in the first week of May, coinciding with a major firmware update.

The Hidden Nuance: The ‘Tone and Mood’ Confounder — here, however, we encounter a subtle but profound limitation which is a perfect illustration of where correlational systems can mislead. A basic sentiment model scores text based on keywords such as “love,” “hate,” or “disappointed.” But human expression is far richer. Consider these three customer reviews for a new premium product.

Example 1. “The build quality is fantastic. I love it.” (Sentiment: Clearly Positive.)

Example 2. “For £2,000, I must say the ‘premium’ feel is somewhat… lacking. An interesting choice, for sure.” (Sentiment: A naive model might score this as neutral or even slightly positive due to the absence of overtly negative keywords. However, the tone is dripping with sarcastic disappointment.)

Example 3. “I’ve been a loyal customer for ten years. The product arrived with a scratch. While support was polite, having to go through this process at all for a flagship device is, frankly, disheartening.” (Sentiment: The explicit keywords are “loyal” and “polite.” The negative keyword “scratch” might be outweighed. A simple model could misinterpret this as a mixed-but-acceptable experience. The dominant mood, however, is one of deep disappointment and a breach of trust.)

A purely inductive system risks conflating these very different reviews. It might report that “only 5% of reviews are negative,” while failing to detect that another 20% are expressing deep frustration through sarcasm and polite disappointment. It has correctly identified the keywords but has completely missed the mood. This is a nuanced version of the Causal Parrot problem: the system presents a correlation — “95% of reviews are not explicitly negative” that masks a deeper, more dangerous causal truth: a significant portion of the customer base is becoming disillusioned.

The platform has done its job masterfully, but its limitations define the next challenge. It has brought the organisation to the middle of the Intelligence Ladder, armed with a powerful but potentially misleading map of correlations. It has identified the “what” and the “when,” but the true “why” remains obscured by the nuances of human expression.

Stage 2: Ascending to Causality with an Agentic AI Framework

The second system, architected for the high-stakes world of mission-critical IT incident management, exemplifies how to climb the final, most valuable rungs of the ladder. It is designed not just to observe, but to actively investigate and establish cause.

Its Mandate and Objective: This framework’s core mission is to dramatically reduce the Mean Time to Resolution (MTTR) for critical system outages. Its objective is not merely to alert human engineers to a problem, but to deliver a validated root-cause analysis and a recommended resolution plan. This system is designed to produce an auditable causal record — the very thing required to overcome the trust deficit.

A useful way to understand how such causal discipline is implemented on the ground is to examine the logic of a production-grade Major Incident assessment system. Rather than relying on a monolithic confidence score, the assessor is deliberately built around two fully independent signals. The first captures historical intervention evidence: when incidents like this occurred before, were they actually accepted as Major Incidents? The second captures present-day mechanism alignment: does the incident narrative match the failure-pattern clusters historically associated with validated major incidents? Only when these two lines of evidence agree does the system escalate.

This design is more than an engineering convenience; it is a causal safeguard. Historical precedent and present-day semantic mechanism are intentionally separated so that one cannot silently contaminate the other. The system also includes deterministic fast-path overrides for especially critical configuration items, weighting terms by how distinctive they are within each cluster so that generic incident language does not obscure the underlying failure pattern, and a threshold set high enough that no single signal can carry the decision alone. In other words, the architecture forces corroboration before action.

What makes this especially relevant to the broader argument of this article is that such a system does not merely “score severity.” It operationalises a primitive but highly effective form of causal reasoning: it asks whether a known intervention class has historically applied under similar conditions, whether the same mechanism appears active now, and whether the evidence is strong enough to justify escalation in a way that can later be audited and defended.

Architecture and Reasoning in Action: This system operates as a collaborative team of specialised AI agents, all coordinated by a central Orchestrator Agent. When a high-severity incident is declared — “The checkout service is experiencing a 90% failure rate” — the Orchestrator launches a formal, structured investigation.

Evidence Triangulation: The Orchestrator immediately dispatches its specialist agents. The Ticket History Agent performs analogical reasoning. The Change History Agent applies temporal reasoning. The Knowledgebase Agent provides structural context.

This pattern of evidence triangulation is mirrored in simpler but highly effective operational systems. In the Major Incident assessor, the analogue of the Ticket History Agent appears in the retrieval-based precedent signal, while the analogue of the Knowledgebase and Validation layers appears in the mechanism-matching signal derived from historically validated failure clusters. Even in the absence of a fully explicit Structural Causal Model, the system is already constructing a constrained causal picture: what happened before, what appears to be happening now, and whether the two align strongly enough to justify action.

This is an important point for enterprise practitioners. Causality in production does not always start with formal causal discovery. More often, it starts with disciplined architecture: separating signals, weighting evidence appropriately, defining intervention thresholds, and ensuring that the final verdict is explainable in natural language rather than buried inside a latent score.

Hypothesis Generation (Abduction): The Orchestrator receives this multi-faceted evidence. For instance, it gets: From Change History: “Deployment CHG1138 (a patch for the shipping-and-tax-microservice) was completed at 14:32:05Z.” From Knowledgebase: “The checkout-service has a hard, synchronous dependency on the shipping-and-tax-microservice.” The Orchestrator uses abductive reasoning to synthesise these facts into a primary hypothesis: “The checkout service failure is most likely caused by the recent deployment CHGXXXX.”

Causal Validation (The Hunt for Proof): This is the pivotal step that directly addresses the faithfulness gap. The process, detailed in the blueprint below, now moves from hypothesis to a direct search for proof by activating a Causal Validation Agent. This agent connects to observability platforms and performs a targeted forensic analysis, seeking a “smoking gun” such as a new fatal error message that appeared for the first time only seconds after the deployment.

The Auditable Verdict: If such proof is found, the agent returns a VALIDATED_CAUSE verdict, complete with evidence artefacts. This creates a defensible chain of reasoning that stands up to scrutiny. As Joel Sherlock, CEO of Causify.ai, notes, “Just saying ‘AI told me to do it’ is not a trustworthy governance or audit strategy.” This framework is designed to provide the auditable proof that governance demands.

The Outcome: A Defensible ‘Why’: This framework delivers what every CIO and compliance officer needs: a defensible, evidence-backed causal explanation. By presenting this validated causal chain to a human-in-the-loop for final approval before a Resolution Agent recommends a counterfactual fix — “Roll back deployment CHGXXX” — the system completes the entire journey up the Intelligence Ladder in a matter of minutes, not hours.

Blueprint for Causal Validation: Engineering a System That Knows ‘Why’

The pathway from a plausible hypothesis to a validated causal fact is not magic; it is a feat of disciplined engineering and sound scientific method, executed at machine speed. Operationalising a Causal Validation Agent within the broader agentic framework is not about creating an opaque black box. Instead, it is about instantiating a set of state-of-the-art best practices for digital forensic analysis and causal inference. The following principles form the blueprint for such a system.

1. The Principle of Focused Inquiry: The Mandate of the Specialist

A core best practice in any advanced diagnostic system is the avoidance of “boiling the ocean.” A Causal Validation Agent should not be a generalist tasked with finding a needle in a haystack; it should be a specialist surgeon, given a precise location to make a targeted incision. This principle is operationalised by ensuring the agent is only activated when a higher-level Orchestrator has already performed the initial triage and formed a high-confidence, falsifiable hypothesis. The Orchestrator does not ask, “What’s wrong?”; it makes a specific request: “I hypothesise that deployment CHG1138 to the shipping-and-tax-microservice is the cause of the checkout failure. Investigate this specific causal link.”

This focused mandate is critical. It provides the Validation Agent with the essential constraints — a causal candidate, targeted systems, and a precise time window — that make its task tractable and efficient. This act of constraining the problem space is the first and most important step in moving from a sprawling, time-consuming investigation to a rapid, surgical verification.

A related implementation lesson from production systems is that focused inquiry must also be matched by evidence orthogonality. In the Major Incident assessor, this is achieved by ensuring that historical precedent and incident semantics are computed through separate pathways and only fused at the decision boundary. That separation is not merely modular design; it is a guard against spurious confidence. A system that derives all of its certainty from one representational regime — whether lexical similarity, vector proximity, or model fluency — will always be more vulnerable to correlated failure modes than one that demands agreement across distinct forms of evidence.

2. The Principle of Forensic Analysis: The Search for the Anomaly

With a focused inquiry in hand, the Validation Agent must act as a digital forensic scientist. Its methodology is grounded in a classic technique: comparing the state of the system during the incident to its state before the incident to find a meaningful deviation. First, the agent must establish a “peacetime” baseline. It queries the target systems for a comparable period prior to the suspected causal event to profile the system’s normal heartbeat — its typical error rates, transaction latencies, and log patterns. This crucial step prevents the agent from misattributing pre-existing, benign conditions as causal.

Armed with this baseline, the agent hunts for the anomaly within the designated incident window. It seeks two primary forms of evidence. Novelty Signals (the “smoking gun”) capture events or signatures that simply did not exist in the baseline period — a never-before-seen fatal stack trace, a new security alert, or a catastrophic database failure message. Deviation Signals (the “heart attack”) capture dramatic, statistically significant shifts in metrics, such as a sudden jump in HTTP 503 errors or a multi-order-of-magnitude increase in database latency immediately after a deployment. Both forms of evidence provide powerful causal support when tightly synchronised with the suspected trigger.

The same logic applies outside observability-heavy forensic systems. In operational decision engines, “the search for the anomaly” often becomes the search for discriminative mechanism-level features. In the assessor, this appears through curated stop-word removal, cluster-level inverse document frequency weighting, and semantic-plus-lexical matching that privileges rare, causally meaningful terms over generic incident vocabulary. This is the implementation-level equivalent of saying: not every correlation deserves equal evidential weight, and not every match is a mechanism.

3. The Principle of Evidential Weighting and Auditable Verdicts

A robust causal system does not treat all evidence equally. The final stage of the agent’s logic involves weighing the evidence it has found and delivering a structured, auditable verdict back to the Orchestrator. This is not a simple binary yes or no.

Best practice dictates that the agent should score and categorise its findings based on their causal strength. A single, novel, fatal stack trace is dispositive evidence and carries immense weight. A massive and sustained spike in a known error type is highly compelling. A slight increase in general warning messages, however, might be weak evidence, as it could easily be a secondary symptom rather than a primary cause. Based on this weighting, the agent formulates its verdict. If a smoking gun is found, it returns a VALIDATED_CAUSE status alongside a human-readable summary and direct links or snippets of the key evidence artefacts — the one to three log entries or metric charts that prove the case. If, after a thorough search, no meaningful anomalies are detected, the agent demonstrates non-monotonic reasoning by returning a HYPOTHESIS_REJECTED status, preventing the team from wasting hours chasing a plausible but incorrect theory.

Perhaps the most consequential lesson is that auditable verdicts are not an afterthought; they are part of the causal apparatus itself. In the assessor, the system does not simply emit a numerical score. It produces a structured natural-language justification that explains the contribution of historical precedent, the relevance of matched failure mechanisms, and the reason an escalation is or is not warranted. This is precisely the kind of explanation layer that distinguishes a merely predictive system from one that can participate responsibly in enterprise decision-making.

By the time such a system reaches a verdict, it has already performed a rudimentary but meaningful counterfactual check: would this decision still stand if one of the evidence streams were absent? Because the threshold is intentionally calibrated so that neither signal alone can trigger escalation, the answer is often “no” — and that is a feature, not a weakness. It means the system is designed to withhold action when causal support is incomplete.

Conclusion: From Stochastic Parrots to ‘Wise’ Co-pilots That Understand Real-Word Causality

The enterprise AI journey is maturing beyond the initial phase of generative fluency. Leaders are now asking harder, more practical questions: Can I trust this recommendation? Can we defend this decision in an audit? How can AI help us shape better outcomes, not just analyse past ones? Answering these questions requires a move from correlation-based AI to causation-powered Decision Intelligence.

The strategic path forward, therefore, is to embrace the synergy of Composite AI architectures, which combine the strengths of different models. In this more sophisticated architectural view, LLMs function as the system’s brilliant “limbic brain” — the engine for fluent communication, rapid pattern recognition, and task initiation. Causal AI, in turn, provides the “prefrontal cortex”: the deliberative, structured reasoning engine that can test hypotheses, understand consequences, and construct a logical, defensible argument. As Microsoft Research has stated, “Causal machine learning is poised to be the next AI revolution.”

**This story is brought to you by Ritvik Shyam — Data Scientist & AI Consultant — Member of the AI @ Pace Collective **Contact the TCS Pace London Team or Ritvik on Linkedin