Alarm Suppression is Not Root Cause Analysis

“Root Cause Analysis” (RCA) is one of the most overloaded terms in modern engineering. Some call a tagged log line RCA. Others label time-series correlation dashboards or AI-generated summaries as RCA. Some reduce noise by filtering or hiding secondary and cascading alarms. And recently large language models (LLMs) have entered the scene, offering natural-language explanations for whatever just broke.

But here is the problem: none of these are actually solving the Root Cause Analysis problem. Alarm suppression is NOT Root Cause Analysis.

For teams operating modern, distributed systems - microservices, data pipelines, container orchestration, multi-cloud dependencies - these limitations aren’t minor. They make it impossible to reason clearly about why performance degradations and failures happen, and how to prevent them.

For example, at a financial services company, a cascading Kafka issue was misdiagnosed for hours as a frontend memory spike. The result? Missed SLOs and three teams paged on a Saturday.

What These Tools Miss

Today's “RCA” tools suffer from several critical limitations:

They can’t infer the root cause unless it’s already present in the observed alarms – i.e., the output must be in the input.
They don't explain why the symptoms occurred – they just hide redundant signals.
They require perfect, complete signals to function reliability.
They often produce misleading or spurious outputs.

At Causely, we’re not building a log aggregator, a correlation engine, alarm suppression, nor a chatbot. We are building a causal reasoning system. To understand how this approach is different and why it matters, we first need to re-establish what “root cause analysis” is meant to solve.

What is the Root Cause Analysis Problem?

In managed cloud environments, the RCA problem requires the identification of the most likely cause of observed symptoms, based on a structured understanding of the environment and causal interdependencies between services. Put simply: it’s about using what you know about your system to explain what you see—not just matching alerts to patterns, but reasoning through cause and effect.

What Most RCA Tools Actually Do

Most tools that claim to perform RCA fall into one of three categories:

Postmortem Narratives: These are after-the-fact writeups, often shaped more by human bias than data, constructed to provide a retrospective of what went wrong.
Correlation Engines: These systems surface anomalies and related signals during incidents but confuse correlation with causation. They provide visibility into what things happened around the same time, but they don’t know why.
LLM-Powered Assistants: These interfaces can produce plausible-sounding explanations by summarizing the data they have access to, but they often generate spurious or unverifiable answers.

While all three of these approaches can be useful in some ways, none of them utilize structured causal knowledge and reasoning to solve the RCA problem. That’s the missing piece.

What Actual Causal Analysis Looks Like

To be clear, causal analysis is not about finding "what’s weird." It’s about inferring what is the root cause. At Causely, our reasoning platform is built on three foundational principles:

1. Root Causes Are Explicitly Defined

A root cause is an underlying issue that results in multiple degradations and disruptions in the managed environment. Formally, a root cause is defined by causes and effects, where the cause is the underlying issue (e.g., a DB experiencing inefficient locking), and the effects are the disruptions it creates in the environment (e.g., degraded service response times).

Our system monitors your environment and identifies which anomalous behaviors (such as high error rates, slow responses, or service crashes) are symptoms, using metrics gathered from telemetry and observability tools.

Causely represents each root cause as a closure - a signature uniquely defined by a specific set of expected symptoms. Each root cause and its closure are automatically generated from causal knowledge and the discovered topology. Utilizing Causal Bayesian Networks, Causely can effectively and accurately infer root causes by reasoning over the relationships between the root causes and symptoms, rather than relying on simple mapping or correlations.

Causely constructs precise causal graphs that show not just what broke, but why. This allows issues to be resolved faster and more efficiently.

2. Causal Reasoning Is Bayesian

Causely infers the root cause based on observed symptoms, even when the observations are incomplete or noisy using causal Bayesian networks. Why Bayesian networks? Because you can’t assume perfect signals and should always assume that the observed symptoms will be noisy:

Symptoms aren’t always fully observable.
Real systems behave unpredictably.
And we need to reason probabilistically under uncertainty.

Causely uses Bayesian causal graphs to represent possible root causes and their effects, assigning probabilities to capture uncertainty in real-world systems. The prior probabilities in our models are defined by experts with decades of experience in distributed systems, microservices, data pipelines, and container orchestration. During active incidents, Causely uses observed symptoms to calculate posterior probabilities over possible root causes. These probabilities are then used to identify the most likely root cause, enabling teams to respond quickly and decisively.

3. Causal Graphs Are Customer-Specific

Our causal models are not static or generalized templates. Causely automatically constructs topologically grounded causal graphs that are specific to the customer’s environment and dynamically adapt as environment services and dependencies change. The causal graphs map environment-specific causal dependencies and represent how root causes propagate and manifest themselves across services.

Benefits of the Causely Approach

Causely conducts root cause analysis in a principled way to ensure:

High Precision

Root causes are curated and relevant to managed cloud environments.
Causal structures mirror actual deployment topologies.
False positives are mitigated by never guessing outside the defined causal space.

Generalizability & Rapid Deployment

Bayesian methods identify root causes even with sparse observations.
Topologies are grounded in causal graphs to ensure root cause accuracy.
Dynamic topology updates with real-time telemetry data let us adapt to each customer’s specific patterns.

Predictive Power

Causal graphs are constructed a priori to ensure the reasoning engine can predict which downstream symptoms and disruptions may emerge when monitoring active root causes.
Causely’s causal graphs enable corrective interventions before all root cause symptoms manifest.
In addition to being used for real-time operations, the causal reasoning system can also be used to anticipate future failures and help prevent them. Causely identifies critical services and their failure risks based on causal pathways.

What About LLMs?

Strengths of LLM-Based RCA

LLM-based approaches to RCA are gaining traction. They offer unique strengths:

Natural Language Interface: You can ask follow-up questions in plain English and get responses without needing to write a query or scan dashboards.
Trained on Broad Knowledge: LLMs draw from a vast, pre-trained corpus. Most LLM’s knowledge spans Stack Overflow, GitHub issues, and decades of online technical discourse. This breadth allows them to generate plausible explanations across diverse technologies.
Rapid Response: Most LLMs will respond within seconds and can complete tedious tasks quickly.

But these benefits come with real tradeoffs.

Limitations of LLM-Based RCA

Spurious Causes: LLMs often draw conclusions that appear coherent but are factually incorrect and contextually invalid - due to hallucinations, logical inconsistencies, and insufficient understanding of the managed environment
Unprincipled Reasoning: LLMs mimic the language of reasoning without performing structured inference. Research shows that LLMs suffer from content effects, where prior biases interfere with logical reasoning and result in reasoning fallacies.
Causal Identification Failures: Research shows LLMs systematically struggle with causal prediction, especially in dynamic settings due to the causal sufficiency problem.

While LLMs are quickly gaining traction, they remain limited in accurately identifying root causes and reasoning in complex environments. Causely combines the best of both worlds by using LLMs responsibly to support natural language conversations, while grounding root cause analysis in structured causal models to ensure precision and accuracy.

RCA Isn’t a Buzzword. It’s a well-defined problem.

If you are calling something a “root cause,” you should be able to show how it caused the observed effects - not just that it co-occurred or provide an explanation that sounds plausible.

At Causely we solve the RCA problem by being structured, explainable, and rooted in decades of engineering expertise. We don’t guess. We infer. We don’t react. We reason.

Let’s stop diluting RCA into dashboards and chatbots. Let’s build systems that actually understand why things break and how to prevent future failures.

If you’re tired of dashboards that guess and chatbots that bluff, it’s time to reason instead. Start your journey with Causely today.

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— The Causely Team