TB: On Causation
Core Thesis
Association and causation are fundamentally different epistemic objects: association is about prediction (what co-occurs), causation is about intervention (what would happen if we acted). Neyman's counterfactual framework makes this precise — causal inference is the problem of reasoning about the unobserved.
Key Takeaways
The core distinction
- "Association is about prediction. Causation is about intervention."
- Two questions that look similar but are logically independent: "Does X predict Y?" vs. "If I do X, will Y happen?"
- A variable can be associated with an outcome without causing it (confounding), and a cause can be hard to detect via association (weak signal, measurement error).
The Neyman counterfactual frame
- Neyman framed causal inference in terms of measuring the unobserved: the effect of a treatment that was not performed.
- "Causal inference, in this view, is the problem of inferring something (the effect of the treatment that was not performed) about unobserved counterfactuals from observed data."
- The fundamental problem of causal inference: for any given unit, you can only observe one potential outcome — the one that actually occurred.
Mental Models
- The Map is Not the Territory — confusing association for causation is using prediction-maps to navigate intervention-territory
See also
- Philosophy and Epistemology — causation as a foundational question in the theory of knowledge
Source
Substack, 2026 Original article