Causal Analytics Platform

Your dashboards show what happened.
We show what caused it.

Causalysis gives marketing, growth, and product teams a rigorous way to measure what actually drives performance — beyond correlation, beyond last-click, beyond guesswork.

Causal Impact — Q4 Campaign

+18.4% Revenue lift

↑ Statistically significant (p < 0.01)

✓ Causal — not correlation

Attribution breakdown

Paid social

Organic

Confidence Interval

Effect: +18.4%
95% CI: [+14.1%, +22.7%]
Method: Causal graph

The Problem

You're making decisions on data that only tells half the story

If any of these sound familiar, your team is flying partially blind — not because of bad data, but because of the wrong kind of analysis.

"Our dashboard shows a spike — but we can't tell if we caused it"

Metrics go up. A campaign ran around the same time. Leadership wants to know if it worked. You genuinely cannot say with confidence — because correlation isn't causation, and your tools don't know the difference.

"Last-click attribution is giving us the wrong answer — and we know it"

Everyone in the room knows last-click overcredits direct and email while ignoring the actual journey. But it's the number leadership tracks. You're optimizing against a metric you don't trust.

"Our A/B test results keep contradicting each other"

One test says yes. The next says no. Novelty effects, interference between variants, and peeking at results mid-test all produce noise that looks like signal. Without the right framework, you can't tell which experiments to believe.

"We're scaling things we can't prove actually work"

Without reliable causal evidence, growth decisions default to gut feel dressed up in dashboard numbers. The result: budget allocated to things that correlated with success during a good quarter — not things that caused it.

A Higher Standard

The industry settled for correlation. You don't have to.

Causal inference methods — the same tools used in economics, clinical trials, and academic research — are now accessible to any growth or analytics team with clean enough data. The gap isn't technical. It's tooling.

Causalysis brings together experiment design, causal graph modeling, and event studies in a single platform — so your team can answer the question that actually matters: did this cause that?

The old way

Run a campaign, check if the metric went up, claim success

Trust last-click attribution because it's easy to set up

Run A/B tests without accounting for interference or peeking

The Causalysis way

✓

Estimate the true causal effect of every campaign and experiment

✓

Understand which channel actually drove conversion — with confidence intervals

✓

Know exactly what to scale, what to kill, and what to test next

The Platform

Everything your team needs to turn experiments into evidence

Know exactly what to test — and trust the results

Design and manage all your experiments in one place. Causalysis enforces statistical best practices and surfaces the analyses most relevant to your data structure.

A/B Tests Event Studies Pre/post Analysis

Get answers you can defend in any meeting

Every experiment analysis includes effect size, confidence intervals, and a plain-English interpretation. Share results that stand up to scrutiny — not just screenshots of upward-trending charts.

Causal Graphs Effect Estimation Uncertainty Bounds

Connect experiments directly to revenue targets

Track cumulative experiment impact against your actual business goals. See not just what moved a metric, but how much it contributed to the numbers your company cares about.

Business KPIs Impact Tracking Growth Dashboard

Get Started

Stop making growth decisions you can't defend. Start making ones you can prove.

A 20-minute demo — no pitch, just a walkthrough of how Causalysis would work with your existing experiments and data.

The Platform

Built for teams that need to know, not just guess

Causalysis combines experiment management, causal inference, and business impact tracking in a single workflow — so every analysis your team produces is rigorous, reproducible, and decision-ready.

Know exactly what to test — and trust the results when you do

Causalysis gives your team a structured environment to design, log, and manage all your experiments. Before analysis begins, the platform guides you to the right methodology for your data — preventing the most common sources of unreliable results.

Automatic detection of bias between experiment variants
Supports A/B tests, causal graphs, synthetic control, and pre/post designs
Experiment registry with full history — never lose context on a past test

Causalysis experiment Analysis view — input, output and guardrail metrics with outcome and next actions

Get answers you can defend in any boardroom

Every analysis in Causalysis produces not just a number, but a complete picture: the estimated effect, the uncertainty around it, and the reasoning behind the methodology chosen. Results are designed to be shared — not just stored.

Causal graph modeling for complex, multi-variable relationships
Event study analysis for campaigns without clean control groups
Effect size with confidence intervals — not just p-values
Plain-English summaries generated alongside the technical output
Shareable reports built for leadership, not just data teams

Causalysis analysis result — backdoor estimation summary with equation, average treatment effect, and causal graph

Connect every experiment to the numbers that matter

Individual experiment results only tell part of the story. Causalysis aggregates impact across all your experiments and maps it against your actual business targets — so you always know which bets are paying off and where the growth is actually coming from.

Cumulative experiment impact view across all active tests
Map results to revenue, retention, activation, or any business KPI
Identify which experiments contributed the most to target achievement
Prioritization signals for what to scale, what to stop, and what to re-test

Causalysis Reports — Treatment Effects dashboard with active users, new users, COGS and gross revenue charts

Workflow

From question to confident decision in three steps

Log your experiment

Define your hypothesis, your data source, and your target metric. Causalysis recommends the right analytical approach based on your setup.

Run causal analysis

The platform estimates the true causal effect of your intervention — accounting for confounders, selection bias, and uncertainty — and flags any validity concerns.

Make a decision you can defend

Get a clear, shareable result: the effect size, the confidence interval, and a business-ready interpretation. Know what to scale, what to kill, what to test next.

See Causalysis on your own data

A 20-minute walkthrough tailored to how your team runs experiments. No sales pressure, no commitment.

Insights

The science of knowing what actually works

Practical guides on causal inference, experimentation, and analytics for growth and product teams.

Causal Inference

Introduction to Causal Inference

When the solution is worse than the problem itself: how an incomplete understanding of cause and effect leads businesses to costly, wrong decisions.

5 min read · Causalysis Team

Experimentation

Experimentation Key Concepts & Best Practices

Good experimentation practices that take you from 0 experiments per week to a consistent flow of weekly experiments.

5 min read · Causalysis Team

Marketing

Leveraging Causal Graphs for Marketing Optimization

Correlation does not imply causation. Causal graphs help marketers understand not just what is happening, but why — and where the budget actually works.

4 min read · Causalysis Team

Pricing

Measuring Price Elasticity

What price elasticity of demand is, why it matters for sales and marketing teams, and the three main approaches to estimating it well.

6 min read · Causalysis Team

Strategy

Metrics Minimalism: 2 Metrics Is All You Need

If you focused 100% of your attention on the two most vital metrics, what should they be — and why acquisitions and lifetime value win.

2 min read · Causalysis Team

Attribution

Why Last-Click Attribution Is Costing You More Than You Think

The gap between what last-click reports and what actually drove your revenue isn't just an analytics problem — it's a resource allocation problem.

8 min read · Causalysis Team

Experimentation

When A/B Tests Break Down: The Cases Standard Tests Can't Handle

Network effects, novelty bias, and interference between variants are more common than most teams realize — and standard A/B frameworks weren't built for them.

11 min read · Causalysis Team

Causal Inference

The Difference Between Correlation and Causation — And Why It Costs Growth Teams

A practical introduction to why the correlation-causation distinction matters for business decisions, and what to do about it without a PhD in econometrics.

9 min read · Causalysis Team

Leadership

Communication

How to Present Experiment Results to Leaders Who Don't Trust the Data

Credibility in experiment reporting isn't built by showing more charts. It's built by showing you understand why your numbers might be wrong — and proving they aren't.

7 min read · Causalysis Team

Methodology

Event Studies for Marketing: Measuring Campaigns Without a Control Group

What do you do when a control group isn't possible? Event study methodology was designed for exactly this problem — and it's underused in marketing analytics.

12 min read · Causalysis Team

Growth

The Experiment Portfolio: How Fast-Growing Teams Manage 50+ Tests at Once

Running many experiments in parallel creates its own risks — interference, resource contention, and conflicting results. Here's how the best teams manage it.

10 min read · Causalysis Team

Ready to apply this to your own experiments?

See how Causalysis can bring causal rigor to the work your team is already doing.

Insights

Article

Causalysis Team

← Back to all articles

Ready to apply this to your own experiments?

See how Causalysis can bring causal rigor to the work your team is already doing.

About Causalysis

Built by people who got tired of making decisions they couldn't prove

Causalysis was started because the methods to answer "did this actually work?" have existed for decades — in academia, in economics, in clinical research. They just weren't accessible to the teams who needed them most.

Our story

Every growth team we'd ever worked with or spoken to had the same problem: they ran experiments, watched metrics, presented results — and somewhere in the back of their minds, they knew they couldn't be sure. The metric went up. Was it the campaign? The season? Something else that changed at the same time?

The tools they were using weren't designed to answer that question. Dashboards show correlation. A/B testing platforms handle simple experiments but fall apart when the real world gets complicated. Attribution models make assumptions that everyone in the room knows are wrong but nobody knows how to replace.

Meanwhile, the methods that could answer the question — causal inference, event studies, causal graph modeling — were sitting in academic papers and applied by specialized economists and data scientists at a handful of large companies. They weren't accessible as a product.

Causalysis exists to change that. We believe that rigorous causal reasoning should be as accessible to a 10-person growth team as it is to a team of PhD economists at a tech giant. The methods shouldn't require a statistics background. The output should be decision-ready, not just technically correct.

Years of research and development in causal inference tooling

Growth and analytics teams interviewed during product development

500+

Experiments analyzed across pilot studies

100%

Focus on causal — not just correlational — analytics

Athens, Greece

We're a small, technical team based in Athens. We believe the best analytical tools are built by people who understand both the statistics and the business context — and that the gap between academic rigor and business application is a design problem, not a difficulty problem.

What We Believe

The principles that drive every product decision

Rigorous before fast

We never sacrifice statistical validity for convenience. If an analysis can't be done correctly with the available data, we tell you — and help you understand why, rather than producing a number that looks good but misleads.

Decisions, not metrics

A number without a decision attached is trivia. Every output from Causalysis is designed to answer a specific question: should we scale this? Stop it? Run it again differently? Analytical rigor in service of action.

Accessible ≠ dumbed down

Making causal inference accessible doesn't mean hiding the complexity. It means presenting it clearly — with uncertainty bounds, methodology explanations, and honest caveats — so teams can engage with the real picture rather than a simplified one.

Built with, not just for

Our best product decisions have come from conversations with growth analysts, marketing scientists, and VPs of Product who use tools like ours every day. We build in close collaboration with our early customers — their problems shape the roadmap.

Want to shape what we build next?

We're onboarding founding customers who get early access, direct input into the roadmap, and hands-on onboarding from our team.

Request a Demo

A 20-minute walkthrough. No pitch. Just clarity.

Tell us a bit about your team and how you currently handle experiments. We'll show you exactly how Causalysis would fit into your workflow.

What to expect

This isn't a sales call with slides. It's a working session where we look at how your team currently runs experiments and what better analysis would look like in practice.

📋

Before the call We'll ask a few questions about your experiment setup and current tools so we can tailor the walkthrough.

🖥️

During the call (20 min) We show Causalysis on a real dataset — ideally similar to yours — and walk through what the analysis would produce.

🚀

After the call If it's a good fit, we'll discuss what early access looks like. If not, you'll leave with a clearer picture of your options regardless.

Also reach us at

Tell us about your team

First name

Last name

Work email

Company

Your role

How does your team currently measure experiment impact?

We respond within 1 business day. No spam, ever.

FAQ

Common questions

Do I need a data science or statistics background to use Causalysis? +

No. Causalysis is designed for growth and analytics practitioners, not statisticians. The platform selects the appropriate methodology based on your setup and presents results in plain language alongside the technical output. That said, having someone on your team who understands experimentation basics (even without formal training) helps you get more value from it.

What data integrations does Causalysis support? +

We're actively building integrations with the most common analytics and data warehouse tools. In the meantime, early access customers work with CSV exports or direct database connections. During your demo, we'll discuss what your data setup looks like and what the integration path would be.

How is this different from running analysis in Python or R ourselves? +

A skilled data scientist can run causal analyses in Python or R — but this requires significant setup, methodology expertise, and time for every new experiment. Causalysis operationalizes that process: it handles methodology selection, validity checks, result formatting, and sharing, so your team can do it repeatedly and reliably without dedicated statistical expertise for each analysis.

We already use Optimizely / VWO / a similar tool. Do we need to replace it? +

Not necessarily. Causalysis is focused on the analysis layer — turning your experiment data into causal evidence — rather than the experiment delivery layer. Many teams use Causalysis alongside their existing testing platform, importing results for deeper causal analysis. We can discuss your specific setup in the demo.

What does early access involve? +

Early access customers get hands-on onboarding, direct access to our team for questions and support, and real input into the product roadmap. In return, we ask for honest feedback and a willingness to share what's working and what isn't. It's a genuine collaboration — not just a beta tag on a finished product.

Your dashboards show what happened.We show what caused it.

You're making decisions on data that only tells half the story

"Our dashboard shows a spike — but we can't tell if we caused it"

"Last-click attribution is giving us the wrong answer — and we know it"

"Our A/B test results keep contradicting each other"

"We're scaling things we can't prove actually work"

The industry settled for correlation. You don't have to.

Everything your team needs to turn experiments into evidence

Know exactly what to test — and trust the results

Get answers you can defend in any meeting

Connect experiments directly to revenue targets

Stop making growth decisions you can't defend. Start making ones you can prove.

Built for teams that need to know, not just guess

Know exactly what to test — and trust the results when you do

Get answers you can defend in any boardroom

Connect every experiment to the numbers that matter

From question to confident decision in three steps

Log your experiment

Run causal analysis

Make a decision you can defend

See Causalysis on your own data

The science of knowing what actually works

Introduction to Causal Inference

Experimentation Key Concepts & Best Practices

Leveraging Causal Graphs for Marketing Optimization

Measuring Price Elasticity

Metrics Minimalism: 2 Metrics Is All You Need

Why Last-Click Attribution Is Costing You More Than You Think

When A/B Tests Break Down: The Cases Standard Tests Can't Handle

The Difference Between Correlation and Causation — And Why It Costs Growth Teams

How to Present Experiment Results to Leaders Who Don't Trust the Data

Event Studies for Marketing: Measuring Campaigns Without a Control Group

The Experiment Portfolio: How Fast-Growing Teams Manage 50+ Tests at Once

Ready to apply this to your own experiments?

Article

Ready to apply this to your own experiments?

Built by people who got tired of making decisions they couldn't prove

Our story

The principles that drive every product decision

Rigorous before fast

Decisions, not metrics

Accessible ≠ dumbed down

Built with, not just for

Want to shape what we build next?

A 20-minute walkthrough. No pitch. Just clarity.

What to expect

Common questions

Your dashboards show what happened.
We show what caused it.