One Planet,
No Controlled Rerun
How can we learn cause and effect from a planet we cannot experimentally rerun?
The storm stalls over Aster Bay
A persistent storm brings intense rainfall. Rivers rise, the saturated ground sheds water quickly, and Aster Bay floods.
What made this event so severe?
The event is the evidence, not yet the explanation.
Several things changed together
Warm water, unusual circulation, abundant moisture, wet soil, and local land conditions were all present. Their co-occurrence describes the event, but does not isolate what difference any one factor made.
Imagine the experiment we wish we could run
Keep the storm setup, circulation, season, and land state comparable. Change the ocean condition, then compare rainfall.
But Earth follows only one history
We observe rainfall under the ocean condition that occurred. We do not simultaneously observe rainfall under the alternative condition.
EFFECT
Where can causal information come from?
We cannot observe the missing Earth directly. But variation, timing, causal structure, and physical knowledge can constrain what the alternative would have implied.
Model experiments are one possible source of evidence. They are not causal inference by themselves.
Repeated variation
Conditions vary across times, places, and events. Those natural differences can create informative comparisons.
Relationships can play different causal roles
Ocean state, circulation, moisture, rainfall, soil, and flooding form pathways, not a flat list of correlated variables.
Does ocean state influence rainfall through moisture, or do both mainly reflect circulation?
Correlation alone does not reveal the pathway. Causal reasoning requires structure.Ocean conditions may affect rainfall indirectly by changing atmospheric moisture.
Not every causal question can be answered from the available data
Different causal structures can produce similar observed correlations. More measurements help only when they distinguish the competing explanations.
Can the observations distinguish them?
Sometimes yes, sometimes only with additional assumptions, and sometimes not. Then the causal effect is not identifiable from the available information.
Ocean conditions affect rainfall through moisture; circulation adds another influence.
Circulation drives moisture and rainfall while also shaping ocean-related conditions.
One history can still support causal knowledge
We cannot rerun the planet. But variation, timing, structure, physics, and explicit assumptions can still constrain how one part of the system influences another.
Causation requires comparison
Knowing what happened is not enough. Causal questions ask what would change under an intervention.
The alternative is not observed
Counterfactual reasoning requires evidence, structure, mechanisms, and assumptions.
Causal knowledge has limits
Some effects can be estimated, some only bounded, and some cannot be identified.
Causal knowledge is constructed from evidence, structure, physics, and explicit assumptions.
Covariance and Correlation
Why variables that change together do not necessarily reveal what happens under intervention.
Coming Soon →From Pattern to Trust
Explore models, mechanisms, evidence, causal reasoning, and bounded trust.
Open the Exploration →When Rare Events Stop Being Rare
How probability, feedbacks, and thresholds reshape extreme-event risk.
Return to the story →When Models Disagree
What disagreement between plausible models reveals about assumptions and uncertainty.
Coming Soon