1 From Association Rules To Causality Presenters: Amol Shukla, University of Waterloo Claude-Guy...

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From Association Rules To Causality

Presenters:

Amol Shukla, University of Waterloo

Claude-Guy Quimper, University of Waterloo

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Limitations of Association Rules and the

Support-Confidence Framework

Generalizing Association Rules to Correlations

Scalable Techniques for Mining Causal

Structures

Applications of Correlation and Causality

Summary

Presentation Outline

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Review: Association Rules Mining

Itemset I={i1, …, ik} Find all the rules XY with min confidence and support

support, s, probability that a transaction contains XY

confidence, c, conditional probability that a transaction having X also contains Y, i.e., P(Y|X)

Let min_support = 50%, min_conf = 50%. Two example association rules are:

A C (50%, 66.7%)C A (50%, 100%)

Transaction-id

Items bough

t

10 A, B, C

20 A, C

30 A, D

40 B, E, F

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Limitations of Association Rules using Support-Confidence Framework

Negative implications or dependencies are ignored

Consider the adjoining database. X and Y: positively related, X and Z: negatively related support and confidence of X=>Z dominates

Only the presence of items is taken into account

X 1 1 1 1 0 0 0 0Y 1 1 0 0 0 0 0 0Z 0 1 1 1 1 1 1 1

Rule Support ConfidenceX=>Y 25% 50%X=>Z 37.50% 75%

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Limitations of Association Rules using Support-Confidence Framework

Another market basket data example Buys Tea => Buys Coffee (support=20%,confidence=80%) Is this rule really valid? Pr(Buys Coffee)=90% Pr(Buys Coffee|Buys Tea)=80%

Negative correlation between buying tea and buying coffee is ignored

Items Boug

ht

Coffee

No Coffee

Sum(row

)

Tea 20 5 25

No Tea

70 5 75

Sum(col.)

90 10 100

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Limitations of Association Rules and the Support-

Confidence Framework

Generalizing Association Rules to

Correlations


Structures


Summary

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What is Correlation?

P(A): Probability that event A occurs P(A’): Probability that event A does not occur P(AB): Probability that events A and B occur

together. Events A and B are said to be independent if P(AB) = P(A) x P(B) Otherwise A and B are dependent Events A and B are said to be correlated if any of AB, A’B , AB’, A’B’ are dependent A correlation rule is a set of items that are

correlated

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Computing Correlation Rules: Chi-squared Test for Independence

For an itemset I={i1,…,ik}, construct a k-dimensional contingency table R= {i1,i1’} x … x {ik,ik’}

We need to test whether each cell r= r1,…,rk in this table is dependent

Let O(r) denote the observed value of cell r in this table, and E(r) be its expected value.

The chi-squared statistic is the computed as:

Rr rE

rErO

)(

)()( 22

If 2 = 0, the cells are independent. If 2 > cut-off value,reject the independence assumption

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Example: Computing the Chi-squared Statistic

Coffee

No Coffee

Sum(row)

Tea 20 5 25

No Tea

70 5 75

Sum(col.)

90 10 100

E(Coffee,Tea)= (90 x 25)/100 = 22.5

E(No Coffee,Tea) = (10 x 25)/100 = 2.5

E(Coffee,No Tea)= (90 x 75)/100 = 67.5

E(No Coffee,No Tea)=(10 x 75)/100=7.5

Since this value is greater than the cut-off value (2.71 at 90% significance level), we reject the independence assumption

2 = (20-22.5)2/22.5 + (5-2.5)2/2.5

+

(70-67.5)2/67.5 + (5-7.5)2/7.5

= 0.28 + 2.5 + 0.09 + 0.83 = 3.7

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Determining the Cause of Correlation

I(r)>1 indicates positive dependence and I(r)<1 indicates negative dependence

The farther I(r) is from 1, the more a cell contributes to the 2 value, and the correlation. Coffe

eNo

Coffee

Tea 0.89 2

No Tea

1.03 0.66

Measures of Interest

Define measures of interest for each cell I(r) = O(r) / E(r)

Thus, [No Coffee,Tea] contributes the most to the correlation, indicating that buying tea might inhibit buying coffee

Cell Counts

Coffee

No Coffee

Tea 20 5

No Tea

70 5

= 70/67.5

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Properties of Correlation

If a set of items is correlated, all its supersets are also correlated. Thus, correlation is upward-closed

We can focus on minimal correlated itemsets to reduce our search space

Support is downward-closed. A set has minimum support only if all its subsets have minimum support

We can combine correlation with support for an effective pruning strategy

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Combining Correlation with Support

Support-confidence framework looks at only the top-left cell in the contingency table. To incorporate negative dependence, we must consider all the cells in the table

Combine correlation with support by defining “CT-support”

Let s be a user specified min-support threshold. Let p be a user-specified cut-off percentage value

An itemset I is CT-supported if at least p% of the cells in its contingency table have support not less than s

An itemset is significant if it is CT-supported and minimally correlated

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A level-wise algorithm for finding correlation rules

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Steps performed by the algorithm at level k

Mark the itemsetas ‘significant’

Is the Itemset CT-supported?

Is 2 greater than cut-off value?

No

Yes Add to the setNOTSIG

Construct ContingencyTable for next itemset

at the level

No

Yes

Generate itemset(s) of sizek+1 such that all of its subsets are in NOTSIG

Done processing all itemsets at level k

Start

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Limitations of Correlation

Correlation might not be valid for ‘sparse’ itemsets. At least 80% of the cells in the contingency table must have expected value greater than 5.

Finding correlation rules is computationally more expensive than finding association rules.

Only indicates that the existence of a relationship. Does not specify the nature of the relationship, i.e., the cause and effect phenomenon is ignored.

Identifying causality is important for decision-making.

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From Association Rules to Causality




Correlations


Structures


Summary

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Causality

Hot-Dogs

Hamburgers33% 33% 33%

Association Rule: Hot-Dogs BBQ Sauce [33%, 50%]Causality Rule: Hamburgers BBQ Sauce

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Bayesian Networks

What is the best topology of a Bayesian network that describes the observed data?

Problem: Very expensive to compute

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Simplifying Causal Relationships

Knowing the existence of a causal relationship is as good as knowing the relationship

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Causality vs Correlation

Two correlated variables can have either:

A common ancestor

A causal relationship

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First Rule of Causality

1) Suppose we have threepair wise dependentvariables:

2) And two variables become independent when conditionedon the third one

Independent

Independent

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First Rule of Causality

Then we have one of these following configurations

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dependent

independent

dependent dependent

Second Rule of Causality

dependent

1) Suppose we havethree variables withthese relationships

2) And the two independent variables become dependentwhen conditioned on the third variable

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Second Rule of Causality

Then the two independent variables cause the third variable.

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Finding Causality

1) Construct a graph whereeach variable is a vertex

2) Perform a Chi-squared testto determine correlation

3) Add an edge labeled “C”for each correlated test

4) Add an edge labeled “U”for each uncorrelated test

5) For each triplet, check if acausality rule can be applied

C C C

C

C

C

C C

U

C

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Weaknesses of the Algorithm

Causality rules do not cover all possible causality relationships

The X2 test with confidence set to 95% is expected to fail 5 times every 100 tests

Some variables might not be reported correlated or uncorrelated

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Generalizing Association Rules to Correlations


Structures

Applications of Correlation and

Causality

Summary

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Experiments (Census)

Correlation rules Not a native English speaker Not

born in the U.S Served in the military Male Married more than 40 years old

Causality Rules Male Moved Last 5 years, Support-Job Native-Amer. $20-$40K House

Holder Asian, Laborer < $20K

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Experiments (Text Data)

416 distinct frequent words 86320 pairs of words, 10% are

correlated Correlation Causality RulesNelson, Mandela upi, not reuterarea, province Iraqi, Iraqarea, secretary, war united, statesarea, secretary, they prime, minister

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Beyond Correlation and Causality

Correlation and causality seem to be stronger mathematical model than confidence and support

It is possible to apply these concepts where confidence and support were previously applied

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Association Rules with Constraints

Correlation can be seen as a monotone constraint

Algorithm obtained by modifying algorithms for mining constrained association rules

At least one item is meat

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Correlations


Structures


Summary

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Conclusion (Good news)

Correlation and causality are stronger mathematical models to retrieve interesting association rules

Allow to detect negative implications

Causality explains why there is a correlation

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Conclusion (Bad news)

Difficult to precisely detect correlation (especially in sparse data cubes)

Not all causality relationships can be found

Are the results really better than with support and confidence?

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Open Problems

How to discover hidden variables in causality

How to resolve bi-directional causality for disambiguatione.g: prime minister minister prime

How do we find causal patterns for more than 3 variables

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References

Papers “Beyond Market Baskets: Generalizing Association Rules

to Correlations” - Brin, Motwani, Silverstein; SIGMOD 97 “Scalable Techniques for Mining Causal Structures” -

Silverstein, Brin, Motwani, Ullman; VLDB 98 “Efficient Mining of Constrained Correlated Sets” -

Grahne, Lakshmanan, Wang; ICDE 2000 “A Simple Constraint-Based Algorithm for Efficiently

Mining Observational Databases for Causal Relationships” - Cooper; Data Mining and Knowledge Discovery, vol 1, 1997

Textbook “Causality: models, reasoning, and inference” - Judea

Pearl; Cambridge University Press, 2000

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Questio

ns

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