Causal Credit Uplift

Phase 1 · Methodology check

Hillstrom MineThatData

I used a real randomized email experiment first to check that the uplift methods work before building synthetic credit data. Mens E-Mail vs No E-Mail, outcome = visit.

Test n

8523

Top-decile overlap

58.1%

Jaccard 0.409

Neg. CATE share

1.4%

123 of 8523 test rows

Policy ranking

Qini curves

CausalForestDMLRandomT-learner

Population fraction targeted

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ModelQiniUplift@10%Uplift@30%Uplift@50%
T-learner7.740.1160.0940.080
CausalForestDML6.260.1530.0860.081

Who gets ranked high

Persuadables vs low-uplift profile

Top decile by predicted CATE vs bottom decile

Recency (mean months)

Top decile

4.3

Bottom decile

5.1

History (mean $)

Top decile

$459

Bottom decile

$302

Mens purchase rate

Top decile

82.3%

Bottom decile

53.4%

Womens purchase rate

Top decile

77.6%

Bottom decile

52.5%

Newbie rate

Top decile

55.9%

Bottom decile

45.7%

Modal history segment

Top decile

3) $200 - $350

Bottom decile

1) $0 - $100

Modal zip

Top decile

Urban

Bottom decile

Suburban

Modal channel

Top decile

Web

Bottom decile

Web

Observed uplift in slice

Top decile

0.116

Bottom decile

0.098

Profiles shown for T-learner (Phase 1 top vs bottom decile).

Model disagreement

They do not pick the same people

Sleeping Dogs candidate

Concentrated negative CATE

T-learner predicted CATE < 0

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FeatureLevelNeg shareTest shareGap
history_segment1) $0 - $10013.0%36.1%-23.1 pp
history_segment6) $750 - $1,00021.1%3.0%+18.2 pp
zip_codeUrban23.6%40.7%-17.2 pp
zip_codeRural31.7%15.1%+16.6 pp
channelMultichannel27.6%12.2%+15.5 pp
history_segment3) $200 - $3506.5%19.6%-13.1 pp

History mean in negative group $555 vs test $240 (2.32× ratio).