The Causal Algorithms Behind Transparent AI

Inside the algorithms that let data justify its own conclusions

Data can be a dangerous current. It needs clarity to navigate it. Image generated with Leonardo AI

Last night, my team and I showcased a prototype at Zurich Insurance Group’s Innovation Festival — an event where data scientists, insurance heads, and startups explore how new technologies can transform insurance. Our project wasn’t about automating faster or predicting more. It was about seeing clearly.

Across industries, the same problem keeps surfacing: data is abundant, but insight is opaque. We’ve built powerful models, but they rarely explain themselves. As a result, organizations find themselves swimming in dashboards that forecast everything, but illuminate nothing.

What we showcased at Zurich was a glimpse of something different — an AI that can show its reasoning. A system where every insight can be traced back through a causal chain, and every decision explains itself.

This article is about that shift: how causal graphs and agentic AI together can bring clarity to the muddy waters of big data. When models stop guessing correlations and start mapping causes, they stop being black boxes. They become transparent currents of insight, as they should be — systems that not only see the world, but understand it.

From Muddy Data Rivers to Explainable Currents

We live in an age of data abundance. Sensors, APIs, and reporting frameworks gush information into every corner of an organization. But abundance doesn’t equal understanding.

I often picture this as a muddy river — fast, restless, full of motion. But hard to see through. Analysts scoop up handfuls of figures hoping the signal is somewhere inside, while decision-makers downstream try to steer by instinct.

What if, instead of chasing bigger data, we focused on clearer data?

What if AI systems could filter out the noise, reveal what truly drives change, and then explain that logic in human language?

That’s the idea behind explainable currents — flows of reasoning that move as fast as data but remain transparent. To reach that state, we need AI architectures that can do two things simultaneously:

A. Understand cause and effect — not just detect correlations.
B. Articulate reasoning — show the sequence of steps that led to a conclusion.

That’s where causal inference and agentic workflows meet.

Why Black Boxes Fail in Regulated Industries

Black-box AI has given us breathtaking predictive power — but also new kinds of blindness.

In sectors like insurance, banking, and sustainability, a model that “just works” isn’t enough. Regulators, auditors, and executives need to understand why it works. Under frameworks like IFRS 17, Solvency II, and the EU AI Act, explainability is no longer optional.

Opaque algorithms may optimize short-term accuracy, yet they create long-term drag:

| Black-Box Model | Transparent Model
—————————|——————————————|—————————————
Accuracy | Often marginally higher | Slightly lower but stable
Auditability | None | Built-in traceability
Adoption | Low — “we don’t trust it” | High — “we can explain it”
Maintenance | Expensive (re-validation cycles) | Easier (visible dependencies)

In practice, I’ve seen brilliant machine-learning prototypes fail because no one could justify their outputs in a risk committee. Predictive opacity becomes operational friction.

The solution isn’t adding more layers of reporting — it’s embedding explainability into the model itself.

That’s exactly what causal inference does.

Causal Graphs as Explainability Engines

A causal graph is a map of how things influence one another.
Where machine learning fits curves, causality draws arrows.

Each node represents a variable; each arrow represents a cause-and-effect relationship. Once you encode this structure, you can not only predict outcomes but explain them — tracing each result back to its underlying drivers.

Here’s a minimal working example using DoWhy:

import dowhy
from dowhy import CausalModel
import pandas as pd

data = pd.DataFrame({
    “investment”: [10, 15, 20, 25, 30],
    “market_growth”: [2, 3, 3, 4, 5],
    “returns”: [5, 7, 9, 10, 12]
})

model = CausalModel(
    data=data,
    treatment=”investment”,
    outcome=”returns”,
    graph=”digraph { market_growth -> investment; investment -> returns; market_growth -> returns; }”
)

identified = model.identify_effect()
estimate = model.estimate_effect(identified, method_name=”backdoor.linear_regression”)
print(”Estimated causal effect:”, estimate.value)

This tiny script doesn’t just compute a regression; it asks a causal question:

“If we changed investment, what effect would that have on returns — holding market growth constant?”

That question is the seed of explainability.
When scaled, these graphs become engines that power every insight in a system.

Visually, they look something like this:

market_growth  →  investment  →  returns
       ↘_____________________↗

Once the structure is explicit, the reasoning becomes inspectable.
Each path from cause to effect is a story the model can tell.

Causal AI gives us structured transparency — a blueprint of logic that can be read, queried, and audited. And once the graph exists, it can be combined with agentic AI to make reasoning dynamic.

Agentic Workflows: From Predictions to Explanations

Large language models (LLMs) have made automation conversational. They can draft, summarize, and suggest. But left unguided, they also hallucinate.
Causal graphs provide the missing compass — a factual structure the agent can reason within.

An agentic workflow is an AI process that breaks complex tasks into explainable steps. When those steps are linked to a causal graph, the result is a traceable reasoning chain.

Instead of a monolithic prediction, we get a dialogue like this:

agent.ask(”Why did reserves increase last quarter?”)
# Output:
# 1. Claims_Frequency ↑ 8% (caused by Weather_Anomalies)
# 2. Loss_Ratio ↑ 5%
# 3. Reserve_Estimate ↑ 3.7%

Here, the agent doesn’t merely report a number; it reconstructs the causal pathway.
Each link is verifiable, each step testable. The same architecture can also simulate interventions:

“If weather anomalies had remained at baseline, reserves would have been 2.9% lower.”

That single line encapsulates both prediction and explanation.

Technically, the workflow looks like this:

1. Observation layer: raw metrics or events (claims, policies, emissions).

2. Causal layer: DAG connecting variables through validated relationships.

3. Agent layer: LLM or rule-based agent querying the causal layer for reasoning.

4. Interface layer: a dashboard or API returning human-readable logic.

Each layer strengthens the others.
The agent gains factual grounding; the graph gains interpretive fluency.
Together, they form the skeleton and voice of transparent AI.

Our Zurich Prototype: Traceable Insight Flow in Action

At Zurich’s Innovation Festival, we demonstrated how this approach reshapes actuarial work.

Traditionally, actuarial reporting involves hundreds of spreadsheets, manual reconciliations, and opaque assumptions.

Our prototype re-imagines that process as a causal dashboard: a live interface where every KPI can be clicked to reveal why it moved.

Instead of “the reserve ratio changed by 3.7%,” the dashboard narrates:

“The reserve ratio increased because claim frequency rose, driven by abnormal weather patterns and delayed settlements.”

Each statement comes directly from a causal graph traversed by the agent — a transparent reasoning chain, not a black-box regression.

For analysts, this changes everything:

• Speed with safety: rapid automation that remains auditable.

• Decision intelligence: understanding which levers truly drive outcomes.

• Cross-team alignment: finance, risk, and sustainability teams share the same causal map.

The collaboration with Zurich continues beyond the festival. The prototype now serves as a foundation for exploring explainable automation across insurance functions — from sustainability risk to capital modeling.

The broader point, however, extends far beyond one company:
Transparent AI isn’t a luxury for insurers; it’s a blueprint for every data-rich organization that wants to see through its own systems.

The Bottom Line: Explainability is Clarity. Clarity is Power

The future of AI isn’t faster prediction. It’s transparent reasoning.

Causal AI gives structure to that reasoning. Agentic AI sets it in motion. Together, they turn opaque systems into explainable flows of logic, where every output carries its own proof of origin. That’s what we showcased this week with Zurich Insurance Group: a small step toward AI that doesn’t just compute, but communicates understanding.

When models can explain themselves, trust becomes automatic. Teams can challenge, audit, and build on insights instead of fearing them. The real prize isn’t interpretability for compliance; it’s clarity for progress.

In the coming decade, every complex decision — from risk modeling to climate strategy — will depend on this shift.

Because when the water clears, we stop drowning in data.
We start navigating it.