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Data mining for causal inferenceAMIT SHARMA Postdoctoral Researcher, Microsoft Research

(Joint work with JAKE HOFMAN and DUNCAN WATTS, Microsoft Research)

http://www.amitsharma.in@amt_shrma

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My research Analyzing the effect of online systems

◦ Recommender systems [WWW ’13, EC ’15, CSCW ‘15]◦ Social news feeds [CSCW ‘16]◦ Web search

Methodological◦ Threats to large-scale observational studies [WWW ’16b]◦ Mining for natural experiments [EC ‘15]◦ New identification strategies suited for fine-grained data◦ Testing assumptions for validity of an instrumental variable◦ Gaps between prediction and understanding [WWW ’16a, ICWSM ‘16]

What is the effect of a recommender system?

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How much do they change user behavior?

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Naively, up to 30% of traffic comes from recommendations

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Naively, up to 30% of traffic comes from recommendations

“Burton Snowboard, a sports retailer, reported that personalized product recommendations have driven nearly 25% of total sales since it began offering them in 2008. Prior to this, Burton’s customer recommendations consisted of items from its list of top-selling products.”

Almost surely an over-estimate of the actual effect, because of correlated demand between products.

Example: product browsing on Amazon.com

Example: product browsing on Amazon.com

Example: product browsing on Amazon.com

Counterfactual browsing: no recommendations

Counterfactual browsing: no recommendations

Problem: Correlated demand may drive page visits, even without recommendations

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The problem of correlated demand

Demand for winter

accessories

Visits to winter hat

Rec. visits to winter

gloves

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Goal: Estimate the causal effect

Causal

Convenience

OBSERVED CLICK-THROUGHS WITHOUT RECOMMENDER

Convenience

?

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Ideal experiment: A/B Test

Treatment (A)Control (B)

But, experiments:may be costlyhamper user experiencerequire full access to the system

Can we derive an observational strategy to identify the causal effect of recommendations?

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Using natural variations to simulate an experiment

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Studying sudden spikes, “shocks” to demand for a book

[Carmi et al. 2012]

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The same author’s recommended book may also have a shock

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Past work Uses statistical models to control for confounds Carmi et al. [2012], Oestreicher and Sundararajan [2012] and Lin [2013] construct “complementary sets” of similar, non-recommended products.

Garfinkel et. al. [2006] and Broder et al. [2015] compare to model-predicted clicks without recommendations.

But, 1. These assumptions are hard to verify.2. Finding examples of valid shocks requires ingenuity

and restricts researchers to very specific categories

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This talk: Using data mining for natural experiments

I. Data-driven instrumental variables

“Shock-IV” method: Mining for sudden spikes (“shocks”) in data

II. General data-driven identification strategy for time series data “Split-door” criterion: Generalizing the idea of shocks

Throughout, we will use Amazon’s recommendation system as an example.

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I. Shock-IV: Mining for valid natural experiments

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Distinguishing between recommendation and direct traffic

All visits to a product

Recommender visits Direct visits

Search visits

Direct browsing

Proxy for unobserved demand

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The Shock-IV strategy: Searching for valid shocks

? ?

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The Shock-IV strategy: Filtering out invalid shocks

Search for products that receive a sudden shock in their traffic but direct traffic for their recommendations remains constant.

Why does it work? Shock as an instrumental variable

Demand

Focal visits (X)

Rec. visits (Y)

Sudden Shock

Directvisits (Y)

Computing the causal estimate

Increase in recommendation clicks (Δr)

Causal CTR (ρ) = Δr/Δv

*Same as Wald estimator for instrumental variables

Increase in visits to focal product (Δv)

Application to Amazon.com, using Bing toolbar logs

Anonymized browsing logs:

• 23 million pageviews

• 1.3 million Amazon products

• 2 million Bing Toolbar users

Sept 2013-May 2014

Recreating sequence of page visits by a user

Search page Focal product page Recommended product page

Recreating sequence of page visits by a user

Timestamp URL2014-01-20 09:04:10

http://www.amazon.com/s/ref=nb_sb_noss_1?field-keywords=George%20saunders

2014-01-20 09:04:15

http://www.amazon.com/dp/0812984250/ref=sr_1_1

2014-01-20 09:05:01

http://www.amazon.com/dp/1573225797/ref=pd_sim_b_2

Recreating sequence of page visits by a user

Timestamp URL2014-01-20 09:04:10

http://www.amazon.com/s/ref=nb_sb_noss_1?field-keywords=George%20saunders

2014-01-20 09:04:15

http://www.amazon.com/dp/0812984250/ref=sr_1_1

2014-01-20 09:05:01

http://www.amazon.com/dp/1573225797/ref=pd_sim_b_2

User searches for George Saunders

User clicks on the first search result

User clicks on the second recommendation

I. Weekly and seasonal patterns in traffic, nearly tripling in holidays

II. 30% of all pageviews come through recommendations

III. Books and eBooks are the most popular categories by far

IV. Apparel and shoes see a substantially higher fraction of visits through recommendations

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Shock-IV: Finding shocks in user visit data

We look for focal products with large and sudden increases in views relative to typical traffic.

Size of shock exceeds:◦ 5 times median traffic◦ Shock exceeds 5 times the previous day's traffic and 5 times the

mean of the last 7 days.

Shocked product has: ◦ Visits from at least 10 unique users during the shock◦ Non-zero visits for at least five out of seven days before and after

the shock

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Shock-IV: Ensuring exclusion restriction

Recommended product (Y) should have constant direct visits during the time of the shock.

(1-β): Ratio of maximum 14-day variation in visits to a recommended product to the size of the shock for the focal product.

Direct traffic to Y is stable relative to the shock to the focal product.

β = 1 Direct traffic to Y is no less varying than the shock to focal product.

β = 0

How to choose

Focal product visits Rec. product direct visits

Focal product visits Rec. product direct visits

Accept

RejectSelect

Using the method, obtain >4000 natural experiments!

20% of all products that had visits on any single day.

Estimating the causal clickthrough rate ()

ρ =Δrxyt*/ Δvxt*

At β = 0.7, causal CTR =3%.

Causal click-through rate by product category

What fraction of the observed click-throughs are causal?

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Estimating fraction of observed click-throughs that are causal

Compare the number of estimated causal clicks to all observed recommendation clicks (non-shock period).

λ = ρxy.vxt / rxyt

Only a quarter of the observed click-throughs are causal

At β = 0.7, only 25% of recommendation traffic is caused by the recommender.

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Generalization? Shocks may be due to discounts or sales

Lower CTR may be due to the holiday season

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Local average treatment effect (LATE), not fully generalizable

Shocked products are not a representative sample of all products, nor are the users who participate in them.

• Fortunately, Shock-IV method covers roughly one-fifth of all products with at least 10 visits on any single day.

• Causal estimates are consistent with experimental findings (e.g., Belluf et. al. [2012])

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Summary: Shock-IV method

I. Mining for instruments allows us to study a much larger sample of natural experiments.

II. Fine-grained data allowed us to test for exclusion restriction directly.

A simple, scalable method for causal inference.◦ Can used for improving recommender systems through causal metrics.◦ Can be applied to other domains, such as online ads.◦ Can be used for finding potential instruments.

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II. Generalizing Shock-IV: “Split-door” criterion

Shocks are traditionally used to identify causal effects, but capture a very rare specialized event.

Let’s have a look at the model again

Demand

Focal visits (X)

Rec. visits (Y)

Sudden Shock

Directvisits (Y)

All we require is that direct traffic to recommended product is not affected by visits to focal product.(no correlated demand)

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Focal Product Recommended Product

Accept

Accept

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The split-door criterion Instead of searching for shocks, Check whether direct traffic for Y is independent of visits to X.

Demand

Focal visits (X)

Rec. visits (Y)

Direct Visits

(YD

More formal: Why does it work?

Can show: Statistical independence of and X guarantees unconfoundedness between X and Y.

Demand

Focal visits (X)

Rec. visits (Y)

Direct Visits

(YD

Two possibilities, both remove the effect of common demand

Demand

Focal visits (X)

Rec. visits (Y)

Dir. visits (YD

Demand

Focal visits (X)

Rec. visits (Y)

Dir. visits (YD

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Sidenote: Split-door criterion generalizes Shock-IV

By capturing shocks, we were essentially capturing notion of independence between X and

Split-door will admit all valid shocks, as also other variations.

Applying to logs from Amazon recommendations

1. Divide up data into t=15 day periods.

2. Find product pairs (X and Y) such that:

: Direct visits to recommended product

Compute ρ =Δrxyt/ Δvxt

Using the split-door criterion, Causal CTR , similar to the estimate from Shock-IV (

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Summary: A general identification criterion

Split-door criterion admits a broader sample of natural experiments than shocks.

Automatically tests for valid identification. Can be used whenever is separable.

Applications: Evaluate the relationship between any two timeseries: e.g. social media and news, ads and search.

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ConclusionMajority of traffic from recommendations may be not causal, simply convenience.Two data-driven methods:• Shock-IV: An IV-based method for mining

exclusion-valid instruments from observational data

• Split-door: A general identification strategy for time series data.

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More generally, data mining can augment causal inference methods

Hypothesize about a natural variation

Argue why it resembles a randomized experiment

Compute causal effect

Develop tests for validity of natural

variation

Mine for such valid variations in

observational data

Compute causal effect

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Thank you!AMIT SHARMA

MICROSOFT RESEARCH@amt_shrma h t tp : / /www.amitsharma. in

Hypothesize about a natural variation

Argue why it resembles a randomized experiment

Compute causal effect

Develop tests for validity of natural variation

Mine for such valid variations in observational

data

Compute causal effect

Sharma, A., Hofman, J. M., & Watts, D. J. (2015). Estimating the causal impact of recommendation systems from observational data. In Proceedings of the Sixteenth ACM Conference on Economics and Computation.