Research-minded team
We are a small and deliberately interdisciplinary group: neuroscience and computational modeling, data analysis, physics, and consulting strategy. One mission holds us together — making explicit the causal structure that data keeps hidden, so that the teams we work with decide on a defensible map rather than on a well-presented correlation.
01
Psychology graduate (UNED) with a master's in Neuroscience from the Institute of Neuroscience of Alicante. On a PhD scholarship from the University of Campania "Luigi Vanvitelli" (Naples), he studies the role of dopamine in decision-making, combining behavioral experiments, computational modeling and neuroimaging. Here he designs the DAGs and decides which causal structure is defensible.
02
He works the data layer everything else rests on: denoising, correlation structure, and parameter recovery. It is the step before any causal claim, because if the signal does not survive the noise, no structure estimated on top of it will hold.
03
Physicist (UNED) and entrepreneur, with a background as operations director and on the commercial side of consulting specialized in research and pharma. He translates between the rigor of the model and the client's decision: he frames the problem, defines the scope of the engagement, and makes sure the result arrives in a form a leadership team can actually use. He is the first point of contact in any conversation.
The initiative
All three of us come from research, and research is where the tools for reasoning about causes were built — tools industry still barely uses. Causality Graphs comes out of exactly that gap: the decision to export that state-of-the-art instrumentation — causal discovery, dynamic modeling, inference under constraints — from the lab to the teams that actually have to make evidence decisions.
Under the FunnyFace SL umbrella, the initiative is also a space to explore a concept that was purely philosophical for centuries and that today, thanks to data science, has become an answerable question: what causes what, and with how much confidence.
The methods we apply are the ones used today in neuroscience and biostatistics labs, not a watered-down version of them.
Causality Graphs is the initiative through which FunnyFace SL brings that instrumentation to pharma and clinical research teams.
What causes what was a philosophical question for centuries. Data science made it answerable, with explicit assumptions and the limits in plain view.
