Most people hear “aging research” and picture someone hunting for a single anti-aging pill. Our view is different, and it starts with a question you can ask in plain English: which changes of aging actually cause the others?
That question is causal inference, and it’s the backbone of our research. This guide is the public tour of how we think about it — no equations, just the logic.
The hallmarks are a list, not a story
In 2013, researchers proposed the hallmarks of aging: a set of processes — genomic instability, epigenetic alterations, cellular senescence, and others — that seem to drive aging across species. Updated in 2023, the framework gives the field a shared vocabulary. You can explore all twelve on our Longevity Atlas.
But a list is not an explanation. Knowing that DNA damage and inflammation and mitochondrial decline all appear with age doesn’t tell you which one matters first. It’s like knowing the parts of a car by name without knowing which one failing stops the engine.
Correlation is not cause
Here’s the trap: almost everything in biology correlates with everything else. Older bodies have more DNA damage, more inflammation, more senescent cells — but that tells us nothing about the direction of the arrows between them.
- Does DNA damage cause senescence, or does senescence create conditions that damage DNA?
- Does chronic inflammation drive aging, or is it a response to damage caused by other processes?
- Which of the twelve, if changed, would change the others?
Those are causal questions. You can’t answer them by listing what correlates. You answer them by building models that make testable predictions about what happens when one part changes.
How we build the model
Our approach is deliberately boring in its ingredients:
- We read the literature at scale. AI structures thousands of papers so every claim can be traced to a source — including claims that contradict each other.
- We draft the causal graph. The twelve hallmarks become nodes; the evidence-supported relationships become directed arrows. No arrow enters the graph without a paper behind it.
- We stress-test the graph. We ask what the model predicts and compare it against independent data. If the model says “this hallmark is upstream,” studies should show its damage appearing earlier.
- We simulate interventions in silico. Before anything touches a human, we ask what the model says happens when a specific process is modified — and we publish the negative results too.
The output is not a claim that we’ve “figured out aging.” It’s an explicit, falsifiable map — one that says exactly what we think drives what, and why, and where we’re not sure.
What this means for you
- For judging claims: when someone says a supplement “targets aging,” the first question is which process, and the second is how do they know that process drives the others. If they can’t answer either, they’re not doing causal science.
- For the field: the causal map is where interventions get their leverage. A compound that slows a downstream consequence may help a little; a lever on an upstream driver has the potential to change much more.
- For our research: the map is the scaffold everything else hangs on. When you see our research programs, this is the first one — causal mapping is not one program among six, it’s the foundation under all of them.
Bottom line: Aging is a network, not a list. The science of aging is the science of the arrows between the hallmarks — and that’s exactly what we model.
Not medical advice. This guide explains a research methodology; it is not guidance on any treatment or health decision.