Posterior probability of uplift
A direct answer to how likely it is that the treatment helped — and how much less comfortable that answer becomes once a threshold that matters is attached.
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Conversion Rate Uplift
The change in conversion rate caused by a variant, on two scales that differ by a factor of the baseline. The relative figure needs its own interval formula.
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Treatment Cost
What applying an intervention costs per unit — including the margin given away to customers who needed no persuading.
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Incremental Revenue
The revenue a treatment caused rather than accompanied. Heavy-tailed, wide-intervalled, and not profit until a margin and a cost are applied.
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Incremental Recovery Rate (IRR)
How much more recovery an intervention produced than doing nothing — a rate, in percentage points, and not a quantity until a population is attached.
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Incremental Sales
The units a campaign actually caused. Why counting them is not the same as counting revenue, and why a promotion can win on one and lose on the other.
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Random Targeting Strategy
Random assignment supports the plain treated-versus-control comparison; random ranking supplies an uplift baseline. Observational estimates need additional identification and adjustment assumptions.
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Causal ML (Causal Machine Learning)
Causal ML relaxes the shape of the model, not the assumptions that make a comparison causal. Those stay exactly as strong as before.
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Cumulative Uplift
The incremental outcome accumulated as targeting goes deeper down an uplift ranking. Why it cannot be a sum over customers, and how to read where it flattens.
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Incremental Gain
The extra outcome produced inside one slice of a ranking. Computed from response rates rather than counts, and not additive across slices when the treated share differs between them.
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Total Incremental Benefit (TIB)
Everything a campaign produced that would not have happened anyway. Converting it to money, carrying its interval, and why treating everyone rarely wins.
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Cumulative Incremental Gain (CIG)
The incremental outcome collected down to a depth of a ranking. Accumulating slice by slice gives a different answer from computing it directly.
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Qini Curve
Radcliffe’s incremental gains chart: treated responders minus what an equal treated group would have produced untreated. Its baselines, and why they matter.
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Uplift Score
The per-unit output of an uplift model: a difference of two predicted probabilities, not a probability. What it supports, and what it cannot be checked against.
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Uplift Random Forests
Trees that split on the effect of treating rather than on the outcome, averaged. The criteria that make the splits, and the interpretability it costs.
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Uplift Curve
Cumulative incremental outcome against the share targeted. Why its vertical axis scales a difference of response rates into an incremental response count, and where to cut the campaign.
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Revenue net of treatment cost
Incremental revenue converted to contribution through the margin, with the campaign’s cost taken out, and the interval the result inherits.
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Incremental Conversions
The conversions a campaign caused, as opposed to the ones it was merely present for. What the scaling to a full population quietly assumes.
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Uplift@k
The incremental effect within the top k percent by predicted uplift — the number that matters when the campaign volume is already fixed.
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AUUC (Area Under the Uplift Curve)
The area under the uplift curve, reduced to a single number — by at least three different conventions that do not agree, on the same model and the same data.
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Qini Coefficient
A single number for an uplift model’s ranking, from the area above the random diagonal. The normalization changes it more than most model differences do.
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Uplift
The difference between the outcome with the treatment and the outcome without it. Why a response model does not estimate it, and what the number is attached to.