Decisions, Causality and All That…

BIG DATAFrom knowing ‘what’ to understanding ‘why’?.

an important decision…

I think she is hot!

Hmm – so what should I write to her to get her number?

The word pretty is a perfect case study for our point. As an adjective, it’s a physical compliment, but as an adverb (as in, “I’m pretty good at sports.”) it is just another word.

On the other hand,

more general

compliments work

quite well.

Source: OK Trends

?

hardships of causality.

Beauty is Confounding

determines both the probability of getting the number and of the probability that James will say it

need to control for the actual beauty or it can appear that making compliments is a bad idea

“You are beautiful.”

causal analysis inonline display advertising.

The life of a browser process.

2. Use observed data to build list of prospects

3. Subsequently observe same browser surfing the web the next day

4. Browser visits a site where a display ad spot exists and bid requests are made

5. Auction is held for display spot

6. If auction is won display the ad

7. Observe browsers actionsafter displaying the ad

1. Observe people taking actions and visiting content

what do advertisers want?Conversions?

RETARGETIN

G

M6D

PROSPEC...

RETARGETIN

G

M6D

PROSPEC...

RETARGETIN

G

M6D

PROSPEC...

0%

2%

4%

6%

8%

10%

12%

14%

RELATIVE LIFT:EXPOSED VS. UNEXPOSED USERS

DID NOT SEE AD SAW AD

CO

NV

ER

SIO

N R

AT

E

1.05X

2.62X

1.11X

1.31X

0.92X2.26X

TELECOM COMPANY A

TELECOM COMPANY B

TELECOM COMPANY C

RETARGETIN

G

M6D

PROSPEC...

RETARGETIN

G

M6D

PROSPEC...

RETARGETIN

G

M6D

PROSPEC...

0%

2%

4%

6%

8%

10%

12%

14%

Conversion Rates

SAW AD

CO

NV

ER

SIO

N R

AT

E

TELECOM COMPANY A

TELECOM COMPANY B

TELECOM COMPANY C

questionof interest.

what is the causal effect of m6d’s display advertisingon customer conversion?

?display advertisingShowing/Not showing a browser a display ad.

customer conversionVisiting the advertisers website in the next 5 days.

P

general approach.

?

Ψ(P)

1. Ask the right question

3. Translate question into a formal quantity

Ψ(Pn) 4. Try to estimate it

2. Understand/express the causal process

What is the effect ofdisplay advertising on customerconversion?

?1. state question.

display advertisingShowing/Not showing a browser a display ad.

customer conversionVisiting the advertisers website in the next 5 days.

P2. express causal process.

O = (W,A,Y) ~ P0

W – Baseline VariablesA – Binary Treatment

(Ad)Y – Binary Outcome

(Purchase)

“You are beautiful.”

data structure: our viewers.

CHARACTERISTICS(W)

TREATMENT(A)

CONVERSION(Y)

Color Sex HeadShape

Ad No Ad

No Yes

Ψ(P) 3. define quantity.

E[YA=ad] – E[YA=no ad]

E[YA=ad]/E[YA=no ad]

additive impact

relative impact

Ψ(Pn)4. estimate quantity.

1.A/B testing

2.Modeling Observational Data

common approach: A/B testing.

Since we can not both treat and not treat the SAME individuals. Randomization is used to create “EQUIVALENT” groups to treat and not treat.

3.4 per 1,000

1.6 per 1,000

practical concerns.associated with doing A/B testing

1. Cost of displaying PSAs to the control (untreated group).

2. Overhead cost of implementing A/B test and ensuring that it is done CORRECTLY.

3. Wait time necessary to evaluate the results.

4. No way to analyze past or completed campaigns.

non invasive causal estimation (NICE).

Estimate The Effects In The Natural Environment (Observed Data)

“what if”causal analysis adjusting for confounding

Need to adjust for the fact that the group that saw the advertisement and the group that didn’t may be very different.

estimation – a primer.1. When can we estimate it? Necessary conditions:

– no unmeasured confounding– experimental variability/positivity

2. Be VERY careful with data collection– Define cohorts and follow them over time

3. Estimation techniques – Unadjusted– Adjust through gA

– MLE estimate of QY

– Double robust combining gA and QY

– TMLE

4. Many tools exist for estimating binary conditional distributions

– Logistic regression, SVM, GAM, Regression Trees, etc.

P(W) P(A|W) P(Y|A,W)

QWQY

gA

TE

CH

NO

LOG

Y A

TR

AV

EL

A

B2B

A

TR

AV

EL

B

TE

LEC

OM

A

TR

AV

EL

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TE

LEC

OM

B

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AV

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RE

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IL A

RE

TA

IL B

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ED

UC

AT

ION

A

TR

AV

EL

F

RE

ST

AU

RA

NT

A

AU

TO

A

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TA

IL C

RE

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AU

RA

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ED

UC

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B

TE

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NO

LOG

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RE

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IL D

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AU

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B

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UC

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FIN

AN

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A

TR

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B2B

B

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I

-100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

-100%

0%

100%

200%

300%

400%

500%

600%

700%

800%

M6D PROSPECTING LIFT RETARGETING LIFT

M6D

PR

OS

PE

CT

ING

LIF

T

RE

TA

RG

ET

ING

LIF

T

summary results.median relative lift of 90%

method validation:A/B Test vs. analytic estimate

method validation: negative test

Impact of Telecommunication company’s advertisement on fast food conversion

gross conversion rates.

Additive Impact

RETARGETING

M6D

PROSPECTING

RETARGETING

M6D

PROSPECTING

RETARGETING

M6D

PROSPECTING

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

0.9%

1.0%

0.5%

0.9%

0.1%

0.8%

0.7%

ADDITIVE IMPACT: EXPOSED VS. UNEXPOSED USERS

AD

DIT

IVE

IM

PA

CT

IN

CO

NV

ER

SIO

N R

AT

E

-0.2%

TELECOM COMPANY A

TELECOM COMPANY B

TELECOM COMPANY C

effectiveness varies by marketer.

RETARGETING PROSPECTING RETARGETING PROSPECTING0%

2%

4%

6%

8%

10%

12%

14%

16%

RELATIVE IMPACT

DID NOT SEE AD SAW AD

CO

NV

ER

SIO

N R

AT

E

B2B COMPANY

A

B2B COMPANY

B

1.08X

1.08X4.23X

3.77X B2B COMPANY A

B2B COMPANY B

RETARGETING PROSPECTING0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

CONVERSION RATEDID NOT SEE AD SAW AD

CO

NV

ER

SIO

N R

AT

E

NO LIFT

NO LIFT

creative matters.

This campaign drove no significant lift from either retargeting or new customer

prospects, likely due to ineffective creative.

Brand is buried; sweepstakes, not the brand, is the primary message

Call to action is inconsistent with primary message

references.1. O. Stitelman, B. Dalessandro, C. Perlich, and F.

Provost. Estimating The Effect Of Online Display Advertising On Browser Conversion. In Proceedings of KDD, Annual International Workshop on Data Mining and Audience Intelligence for Online Advertising, ADKDD ’11.

2. M. van der Laan and S. Rose. Targeted Learning: Causal Inference for Observational and Experimental Data. New York, NY: Springer Publishing Company, 2011. http://www.targetedlearningbook.com/

3. ‘tmle’ R Package http://cran.r-project.org/web/packages/tmle/index.html

4. R. Kohavi and R. Longbotham. Unexpected results in online controlled experiments. ACM SIGKDD Explorations Newsletter, 12(2):31–35, 2010.

5. R. Lewis and D. Reiley. Does retail advertising work: Measuring the effects of advertising on sales via a controlled experiment on yahoo. Technical report, Working paper, 2010.

6. D. Chan, R. Ge, O. Gershony, T. Hesterberg, and D. Lambert. Evaluating online ad campaigns in a pipeline: causal models at scale. In Proceedings of KDD, KDD ’10, pages 7–16, New York, NY, USA, 2010. ACM.

Claudia’s Office Hours:Thursday 2:20 PMExhibition Hall

Data Science Team:Ori StitelmanBrian DalessandroTroy RaederCharlie Guthrie

Foster Provost