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Sure thing

As explained in Causal AI

A sure thing is a person who would experience the favorable outcome regardless of whether the promotion or treatment is applied.

A sure thing is a person who will experience the favorable outcome whether or not an intervention is applied. In promotion targeting, this is a subscriber who will stay with the service with or without a promotion. section 8.3.3

The distinction is causal rather than predictive: a sure thing may have a high probability of staying, but the promotion does not change that outcome. Spending an incentive on this group creates cost without incremental benefit. section 8.3.3

Binary counterfactual segmentation contrasts sure things with:

  • Persuadables, who stay because the promotion changes their decision. section 8.3.3
  • Lost causes, who churn regardless of the promotion. section 8.3.3
  • Sleeping dogs, whose outcome becomes worse when the promotion is applied. section 8.3.3
Figure 8.5 In attempts to influence behavior, we break down the target population into these four segments. Given limited resources, we want to target our influence efforts on the persuadables and avoid the others, especially the sleeping dogs.
figure

Use the population segmentation to compare sure things with persuadables, lost causes, and sleeping dogs when allocating limited influence efforts. section 8.3.3

How Causal AI covers it

How deep it goes
Defines the segment through treatment and no-treatment outcomes, then contrasts its incremental value with three response types.
Examples
Uses subscribers who stay regardless of receiving a churn-prevention promotion.
What it assumes
Assumes binary treatment and outcome reasoning, using promotion versus no promotion and stay versus churn.
What you can do after
Evaluate promotion targets by separating guaranteed favorable outcomes from incremental responders.
How it connects
Connects sure things to uplift modeling, binary counterfactuals, causal effects, persuadables, lost causes, and sleeping dogs.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Causal AI. The section links point to the parts of the book it draws on.