Causal Decision Theory
Updated 2026-09-10
INTRODUCTION
English translation pending.
CORE DEFINITION
Causal decision theory holds that an account of rational choice must use causation to identify the considerations that make a choice rational: within a decision problem it recommends the option maximizing expected utility, where the probabilities used depend on the option chosen itself. The approach was developed by Robert Stalnaker and David Lewis and is contrasted with evidential decision theory.
SCAFFOLDING EFFECT
Reduce cognitive load
- Reason by intervention: evaluate options by asking what happens if I do this, not what the choice indicates. - Strip correlation: separate evidential association from real causation and avoid being misled by symptoms. - Compute utility: weight outcomes by what the action actually causes and pick the highest expected utility.
Anchor fast decisions
The difference lies in how the probability of a state given that I do A is read: causal decision theory uses interventionist conditional probability, so the probability reflects the action's causal influence rather than its evidential significance. Only states the action can causally affect count as sources of value, which explains why it takes both boxes in Newcomb's problem while evidential decision theory takes one.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- plato.stanford.eduhttps://plato.stanford.edu/entries/decision-causal/verified
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