About
Who we are • Our Mission • How we Work
Who We Are
CausalPaths Analytics was founded in Fall 2025 to support a research sub-award from RAND Corporation to UCLA, enabling continued work on NIH-funded projects at the intersection of mathematical modeling, epidemiology, and behavioral science.
Currently led by Dr. Raffaele Vardavas, the organization is designed to grow through collaborative partnerships with domain experts, behavioral scientists, and policy analysts. CausalPaths Analytics provides a flexible structure for advancing research on complex economic and social systems, integrating cognitive and behavioral decision processes with computational frameworks that link individual behavior to population-level outcomes. Through this approach, the organization facilitates interdisciplinary collaboration, supports training for graduate and undergraduate researchers, and contributes scientific insights to inform public policy and preparedness.
Our Mission
We specialize in mathematical modeling of complex systems—mapping structural potential rather than predicting single futures, navigating deep uncertainty rather than eliminating it, and identifying robust pathways through systems characterized by tipping points, cascades, and critical transitions.
While we generate forecasts as part of our analysis, our emphasis is on robust decision-making through structured comparison of policy alternatives. We trust relative differences between strategies more than absolute predictions—policy margins and rank-ordering matter more than point estimates.
We bring mathematical rigor to policy analysis, working across health, economics, social systems, and finance. Read our full mission statement
Our Rates
We offer competitive, transparent pricing for consulting services in mathematical modeling and policy analysis. Our rates are positioned 35–50% below comparable think tank billing rates, with substantial discounts for academic institutions, nonprofits, and long-term partnerships. View our full rate card
Our logo - the brachistochrone under uncertainty
Our logo depicts the brachistochrone curve—the path of fastest descent discovered by Bernoulli, where a particle sliding under gravity reaches the endpoint in minimum time. The curve represents the optimal trajectory given known conditions. But notice how the path begins sharp and certain at the top, then broadens into a band of possibilities toward the bottom.
This widening band captures our philosophy: we can identify optimal or robust pathways, but input parameters, initial conditions, and system structure all carry uncertainty. The "best" path is not a single line but a region of trajectories we must navigate. The point moving along the curve represents a decision-maker descending through that uncertainty—starting from what is known, adapting as the path unfolds, aware that small variations in starting conditions can lead to different endpoints.
The brachistochrone is a mathematical solution to an optimization problem. But in complex systems with deep uncertainty, the solution is not a single answer—it's a map of the terrain, showing which directions remain viable as unknowns resolve. That's what we provide.
How We Work
CausalPaths Analytics operates as a small, agile research consultancy designed to assemble interdisciplinary teams tailored to specific research questions. While currently led by a single principal investigator, the organization draws on an extensive professional network of researchers and former researchers with backgrounds in applied mathematics, economics, epidemiology, behavioral science, and policy analysis—many of whom have collaborated previously in large institutional research environments.
This networked structure allows us to rapidly form project-specific teams with the appropriate combination of expertise, without the administrative overhead associated with large, permanent organizations. Research teams are assembled to match the technical and substantive demands of each project, enabling flexibility in scope, staffing, and timelines.
Operationally, collaborators engage as independent researchers, contracting directly with clients as appropriate. This distributed model emphasizes transparency, efficiency, and scientific focus, allowing resources to be directed toward research effort rather than institutional overhead. The result is a streamlined approach that supports high-quality, policy-relevant analysis while remaining responsive to evolving client needs.
Contact
Raffaele Vardavas, Ph.D.
Principal, CausalPaths Analytics
Email: r.vardavas@gmail.com
Support Our Work
CausalPaths Analytics is committed to advancing mathematical modeling for policy analysis and making these tools accessible to researchers and decision-makers. If you're interested in supporting this work, please reach out to discuss collaboration opportunities.
[Donation information coming soon]