Causal Intelligence for Marketing, Sales & Customer Teams
Why did it happen.
What should you do.
Built for marketing, sales, and customer analytics teams. CausoAI turns your campaign, pipeline, and customer data into causal answers — which channels drive revenue, which interventions reduce churn, which discounts actually close deals.
Who it's for
Marketing Teams
Which channels actually drive conversions — vs. just riding organic demand?
Sales Teams
Are discounts closing deals, or giving away margin on deals that close anyway?
Customer Success
Which onboarding actions causally prevent churn — not just correlate with retention?
Built for marketing, sales & customer teams
Stop guessing. Start knowing what actually works.
Causal Graph Discovery
Automatically learn the causal directed acyclic graph (DAG) from your data using PC, GES, or NOTEARS algorithms.
Causal Readiness Score
5-layer validation engine scores your analysis 0–100 across coverage, identifiability, feasibility, power, and robustness.
Effect Estimation
Auto-select the right estimator — AIPW, PSM, CausalForest, or LinearDML — based on your treatment type and sample size.
What-If Simulator
Run counterfactual scenarios: "What would revenue be if ad spend increased by 20%?" — with confidence intervals.
AI-Powered Insights
Claude generates executive summaries, explains assumptions, and recommends actions in plain English.
Advanced Causal Techniques
Mediation analysis, Difference-in-Differences, subgroup discovery, policy optimization, and root cause analysis — all automated.
How it works
From raw data to causal insight in four steps.
Upload your data
Upload a CSV file with your business data — marketing spend, conversion events, customer records, or sales logs. CausoAI profiles it automatically.
Discover the causal graph
Choose a discovery algorithm (PC, GES, or NOTEARS) and CausoAI learns the causal directed acyclic graph from your data. Edit the graph with a visual editor if needed.
Validate with CRS
The 5-layer Causal Readiness Score tells you exactly how confident you should be in the results — and what gaps need to be addressed before acting.
Get AI-powered insights
Claude generates an executive summary of causal effects, explains assumptions in plain English, and gives you 3+ concrete action recommendations.
By the numbers
Built for speed. Designed for rigor.
< 5 min
Time to first insight
From CSV upload to AI-generated causal analysis
5 layers
Causal Readiness Score
Coverage, identifiability, feasibility, power, robustness
4 algorithms
Graph discovery methods
PC, GES, NOTEARS, and Granger — including time-series
100%
Automated estimation
No data science degree required
Ready to understand your data?
Stop guessing why metrics moved. Start knowing — and acting on — the true causes.