When assumptions meet new evidence
For centuries, the belief that all swans were white seemed justified. Then a single black swan proved otherwise. The metaphor is a reminder that even well-supported assumptions can change when new evidence comes into view.
Black Swan Causal Labs is built around that premise. In real-world health data, the signals that overturn assumptions are often the ones conventional methods miss.
Our Mission
Black Swan Causal Labs exists to find the signals that change practice
We apply AI and causal inference to surface evidence hidden in real-world data, so better decisions reach patients sooner. Our work spans strategy, tooling, and training across the full evidence lifecycle.
What We Offer
AI & RWE Program Design
Strategic engagements that turn "we should be using AI" into a concrete operating model: which evidence questions to automate, what to keep human-led, where the regulatory line sits, and how to govern it.
Agentic AI Architecture & MCP
Multi-agent systems, orchestration layers, and MCP server development for pharmacoepidemiology and RWE workflows. Built to operate inside your data environment, with audit trails regulators can read.
Causal Inference & Study Design
Study design and causal inference that spans the product lifecycle from preclinical through post-licensure: burden of disease, natural history, risk factor characterization, effectiveness, safety, and target trial emulation.
AI Fluency & Enterprise Enablement
Practitioner-led training and advisory that scales RWE teams and the systems around them from AI-curious to AI-fluent, and eventually AI-native. Capability building, workflow redesign, and the governance scaffolding to make adoption stick.
Regulatory & Policy Strategy
Practical interpretation of AI and RWE guidance across the global evidence ecosystem: regulators (FDA, EMA, ICH), payers and HTAs, NITAGs, and bodies like WHO and CIOMS. What to document, what to validate, what to defend in evolving policy environments.
#CAUSALINFERENCE
