# What is uplift modeling?

> Uplift modeling predicts how much a treatment changes an individual's behavior, the causal effect rather than the raw likelihood of an outcome. It sorts people into persuadables, sure things, lost causes, and sleeping dogs, and it targets only the persuadables.

_Last updated 2026-11-01. From The Personalization Guide, https://personalization.guide_

Uplift modeling estimates how much a treatment changes an individual's behavior: the difference
between what they do when treated and what they would have done untreated. That difference, the causal
effect, is a different quantity from how likely someone is to act, and estimating it is what separates
uplift from the more common propensity model.

## What it estimates

A [propensity model](/library/techniques/propensity-scoring) predicts an outcome: how likely this
person is to buy, click, or churn. Uplift modeling predicts the effect of an intervention on that
outcome. For each person it compares two predicted worlds, one where they receive the treatment (an
email, an offer, a discount) and one where they do not, and reports the gap. The technical name for
that per-person gap is the conditional average treatment effect.

The distinction matters because the two questions lead to different spending. Targeting the highest
propensity sends offers to the people most likely to convert, many of whom would have converted anyway.
Targeting the highest uplift sends offers to the people whose behavior the offer actually changes. A
campaign can look efficient on propensity and move almost nothing, because it is paying to reach
decisions that were already made.

## The four groups

Uplift modeling sorts people into four groups by how they respond to being treated:

- **Persuadables** act only if treated. They are the group worth spending on, because the treatment
  causes the outcome.
- **Sure things** act either way. Treating them adds cost and no incremental result.
- **Lost causes** never act, treated or not. Treating them is wasted.
- **Sleeping dogs** are put off by the treatment: they would have acted if left alone, so contacting
  them lowers the outcome. A discount that reminds a loyal customer to reconsider, or a retention email
  that prompts a cancellation, are the everyday examples.

Most of the return hides in spending on persuadables and leaving the other three alone, especially the
sleeping dogs, whom simpler methods cannot even see. A propensity model treats a sure thing and a
persuadable the same, because both look likely to act.

## Why it needs randomized data

Because uplift is a causal quantity, it can only be learned from data where the treatment was assigned
at random, or withheld from a clean holdout. If treatment was decided by the same signals that predict
the outcome, so that the keenest customers got the offer, then who was treated is tangled up with who
was going to act, and no model can separate the effect of the offer from the effect of being the kind
of person who gets offers. This ties uplift closely to [measurement](/measurement): the randomized
experiment that evaluates a program is also the data that trains an honest uplift model.

Common approaches include two-model learners (model the treated and untreated groups separately and
subtract), uplift trees that split on the treatment effect directly, and meta-learners. They are harder
to evaluate than ordinary models, because the per-person effect is never observed for any one person:
you only ever see one of the two worlds. Evaluation leans on grouped comparisons such as Qini or uplift
curves rather than row-by-row accuracy.

## Where it is used

Uplift is most common where the treatment has a real cost or a real risk of backfiring: retention and
churn offers, discount and coupon targeting, and deciding whom to contact under contact limits. It is
the technique that lines up most closely with causal measurement, which is also why it is less common
than propensity. It needs randomized data, larger samples, and more care to evaluate, and many teams
reach for propensity because it is easier, not because it answers the right question.

The practical test for whether a program needs uplift rather than propensity is simple: would some of
the people you are about to target have acted without you? If yes, propensity is paying for outcomes you
already had, and uplift is the method that finds the people you can actually change.