Meaning
Transferring causal inferences from a source population to a target population where experimental conditions differ requires a formal mathematical framework. Causal transportability allows a person who has exited a venture to apply lessons from one market to another without assuming the environments are identical. This property identifies which causal mechanisms remain invariant and which depend on local context.
Inference Transfer
Mechanisms for mapping causal effects involve identifying a set of selection variables that account for differences between the source and target. These variables indicate where the underlying distribution of a cause or effect shifts across the boundary of the original venture. A founder uses this to determine if a specific product growth lever will function in a new geography with different regulatory or cultural norms.
Domain Shift
Successful application depends on the structural similarity of the causal diagrams representing both environments. If the source market has a different causal graph than the target, then simple transport is impossible and a re-weighting or adjustment process is necessary. This measurement protects against the error of assuming that what worked in a high-interest environment is a universal law of business physics.
Boundary Condition
Constraints appear when the target population contains variables that are entirely absent in the source. Directions reverse.
