Does Weather Matter? Causal Analysis of TV Logs

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by  Shi Zong

Does Weather Matter? Causal Analysis of TV Logs
Shi Zong† Branislav Kveton‡ Shlomo Berkovsky§ Azin Ashkan(cid:93) Nikos Vlassis‡
Zheng Wen‡
†TheOhioStateUniversity,Columbus,OH,USA ‡AdobeResearch,SanJose,CA,USA
§CSIRO,Epping,NSW,Australia (cid:93)GoogleInc.,MountainView,CA,USA
[email protected], {kveton, vlassis, zwen}@adobe.com,
[email protected], [email protected]
7 ABSTRACT unitiiscontrolandT = 1iftheunitistreated. Thenunitihas
i
1 twopotentialoutcomes,Y (1)iftheunitistreatedandY (0)other-
Weatheraffectsourmoodandbehaviors,andmanyaspectsofour i i
0 wise. Theunit-levelcausaleffectofthetreatmentisthedifference
life. Whenitissunny, mostpeoplebecomehappier; butwhenit
2 inpotentialoutcomes,τ =Y (1)−Y (0),andtheaveragetreat-
rains, some people get depressed. Despite this evidence and the i i i
r abundanceofdata,weatherhasmostlybeenoverlookedinthema- ment effect on treated (ATT) is Ei:Ti=1[τi] = Ei:Ti=1[Yi(1)]−
a E [Y (0)].NotethatE [τ ]cannotbedirectlycomputed,
M chine learning and data science research. This work presents a i:Ti=1 i i:Ti=1 i
becauseY (0)isunobservedintreatedunits{i:T =1}.
causalanalysisofhowweatheraffectsTVwatchingpatterns. We i i
Sincetheassignmenttotreatmentandcontrolgroupsisusually
show that some weather attributes, such as pressure and precipi-
4 notrandom,E [Y (0)]isapoorestimateofE [Y (0)].A
tation,causemajorchangesinTVwatchingpatterns. Tothebest i:Ti=0 i i:Ti=1 i
2 keychallengeincausalanalysisistoeliminatetheresultingimbal-
of our knowledge, this is the first large-scale causal study of the
impactofweatheronTVwatchingpatterns. ancebetweenthedistributionsoftreatedandcontrolunits.Apopu-
] larapproachtobalancingthetwodistributionsisnearest-neighbor
Y
matching(NNM)[4].Inthiswork,wematcheachtreatedunittoits
1. INTRODUCTION
C nearestcontrolunitbasedontheircovariates,andthentheresponse
. Weather affects our mood, and thus human behaviors. One of ofthematchedunitservesasacounterfactualforthetreatedunit.
s
c thepronouncedexamplesistheseasonalaffectivedisorder–pro- Inparticular,theATTisestimatedas
[ longed lack of sunlight that can depress people [2]. Weather in-
2 dchiraescitnlygbafefheacvtsiovras,riaonudsmasopreec.tsInofthoiusrwloivreks,:wweosrektaonudttsotuedxya,mpiunre- ATT≈ n1T (cid:80)i:Ti=1(Yi(1)−Yπ(i)(0)), (1)
v theeffectsofweatheronTVwatchingpatterns. Inparticular,we wherenT = (cid:80)ni=1Ti isthenumberoftreatedunits,Yi(1)isthe
6 explorewhetherpeoplewatchdifferentgenresofprogramsindif- observedresponseoftreateduniti,andYπ(i)(0)istheobservedre-
1 ferentweatherconditions. sponseofthematchedcontrolunitπ(i).Thecovariateofuniti,xi,
7 Thisknowledgecanhaveseveralimportantimplications. First, shouldbechosensuchthatitspotentialoutcomesarestatistically
8 marketersmaybewillingtoadjustthecontentandratioofthead- independentofTi. Inthiscase,theestimatein(1)resemblesthat
0
vertisementstothetargetaudienceitwillbeexposedto[1]. Sec- ofarandomizedexperiment.
.
1 ond,TVandvideorecommendersystemsmayleveragethisknowl-
0 edgeandadapttheirrecommendationsaccordingly[5].
3. EXPERIMENTS
7 Tothebestofourknowledge,ourworkisthefirsttoapplycausal
1 analysistoanation-scaledatasetofTVconsumptionlogscontain- In this work, we use a dataset gathered by an Australian na-
: ingabout10Mwatchingeventsofmorethan40kusers. Wepro- tional IPTV provider. We obtained the complete Australia-wide
v
i poseseveralpracticalwaysforestimatingthecausalityofweather logsforaperiodof26weeks, fromFebruarytoSeptember2012
X on TV watching behavior, and observe high correspondence be- [5]. Thedatasetcontains10Mviewingeventsofabout40kusers,
r tweenourfindings. whowatchedmorethan11kuniqueprogramsin14TVgenres.We
a also obtained the geographic locations of the users and matched
2. CAUSALANALYSIS themwiththeirweather. Wefurtherextractedeightattributesthat
characterizevariousaspectsofweather(Table1).
Theproblemofestimatingcausaleffectsfromobservationaldata
iscentraltonumerousdisciplines[3]. Itcanbeformalizedasfol- 3.1 ExperimentalSetup
lows. Let{1,...,n}beasetofnunitsi,suchasindividuals. Let
WeapplythecausalityframeworkfromSection2toourdataset.
T ∈ {0,1} indicate the treatment of unit i. That is, T = 0 if
i i Asweexplainoursetup,weillustrateitonanexamplequery“does
hightemperaturecausewatchingmoreDramas?”. Theunitsiare
TVwatchingeventsandweestimatethecausaleffectofweather
(cid:13)c2017InternationalWorldWideWebConferenceCommittee
attheseevents. Thetreatment T indicatesthetreatmentweather
(IW3C2),publishedunderCreativeCommonsCCBY4.0License. i
ateventi. Inourexample, T = 1{hightemperatureateventi}.
WWW’17Companion,April3–7,2017,Perth,Australia. i
ACM978-1-4503-4914-7/17/04. Wedefineonetreatmentvariableforeachweatherattribute,andget
http://dx.doi.org/10.1145/3041021.3054221 eightweather-attributetreatments.Foreachtreatment,wetreatthe
eventsinthetailofthedistributionofthatattribute. Thatis,ifthe
tailofthedistributionisontheleft(right),weassign20%ofthe
. eventswiththelowest(highest)valuestothetreatmentgroup. We
15 Temperature (H) 10 Temperature (H) 10
10 Feels like temp (H) Feels like temp (H)
malized ATT 05 CWloinuPddr s ecpsoesvueerdre (( (HHL))) 04 CWloinuPddr s ecpsoesvueerdre (( (HHL))) 04
Nor Humidity (L) -4 Humidity (L) -4
-5 Visibility (L) Visibility (L)
-10 Preciptation (H) -10 Preciptation (H) -10
DramaSchool KidsComedyDocu Life PanelNews DramaSchool Kids ComedyDocu Life Panel News DramaSchool Kids ComedyDocu Life Panel News
TV genres TV genres TV genres
(a) NormalizedATTsofpressure. (b) StatisticallysignificantchangesinnormalizedATTsforallweather-genrecombinations.
Figure1: (a)NormalizedATTsofpressureon8mostpopulargenreswithTV-genre(blue)andlatent(red)userprofiles. (b)Sig-
nificantchangesinnormalizedATTsofallweathertreatmentson8mostpopulargenres. Significantincreasesarered,significant
decreasesareblue,andinsignificanteffectsaregray.WereportresultsforbothTVgenre(left)andlatent(right)userprofiles.
Weatherattribute Treated Weatherattribute Treated thatmanysignificantchangesinFig.1(b)arestableacrossourtwo
Temperature High(H) Pressure Low(L)
profilerepresentations.Thecorrelationcoefficientbetweentheval-
Feels-liketemperature High(H) Humidity Low(L)
ues in the two plots is as high as 0.949, which indicates that the
Windspeed High(H) Visibility Low(L)
Cloudcover High(H) Precipitation High(H) causaldependenciesarealignedregardlessoftheprofilerepresen-
tation.Thisreaffirmsthevalidityofourresults.
Table1:Ourweatherattributeswiththeirtreatmentgroups. Fig.1(b)revealssomeinsightfultrends. Forexample,whenthe
feels-liketemperatureishigh,weobserveanincreaseinNewsand
listourtreatmentgroupsinTable1.Thevalueof20%ischosenso adecreaseinKidsprograms. Oneplausibleexplanationforthisis
thatthenumberoftreatedeventsissufficientlylarge. that warm days are suitable for outdoor activities and kids watch
Thepotentialoutcomesateventiundercontrolandtreatment, less TV. Therefore, the volume of the genres watched by adults
Y (0)andY (1), aretheindicatorsofwatchedTVcontentunder increases.Whentheprecipitationishigh,weobserveadecreasein
i i
controlandtreatment,respectively.Inourexample, DramasandanincreaseinPanels. Oneplausibleexplanationfor
thisisthatrainydaysmakepeoplelesshappy,andtheywatchless
Y (0)=1{Dramawatchedateventiifthetemperatureislow},
i Dramasthatareunlikelytocheerthemup.
Y (1)=1{Dramawatchedateventiifthetemperatureishigh}. Wealsovalidatethequalityofourmatching. TheaverageEu-
i
clideandistancebetweenthecovariatesofrandomlymatchedevents
Then the ATT in (1) is the change in the frequency of watching
is0.72. Thedistanceofourmatchedpairsis0.13. This82%im-
Dramasduetotemperature. IftheATTissignificantlyabove(be-
provement indicates that our matching is very good, and that the
low)zero, wesaythathightemperatureincreases(decreases)the
estimatedeffectsmaybecausal.
frequencyofwatchingDramas. Anear-zeroATTmeansthatthere
is no effect on Dramas. We define potential outcomes and ATTs
4. CONCLUSIONS
forallcombinationsofTVgenresandtreatments. Becausesome
genresareinfrequent,wereportATTsdividedbythefrequencyof In this work, we conduct a causal analysis of weather on TV
theirgenres. Thisnormalizationisonlyforvisualizationpurposes watchingpatterns. Weusetwouserprofilerepresentationsthatre-
andhasnoimpactonthestatisticalsignificanceofourfindings. vealsimilardependencies.Tothebestofourknowledge,thisisthe
Thecovariatex istheprofileoftheuserateventiandthetime firstcausalanalysisofalarge-scaledatathatlooksattheinterplay
i
oftheevent. Bothstronglyaffect(Y (0),Y (1)),andthereforeare betweenweatherandwatchingTV.
i i
goodcandidatesforguaranteeingthat(Y (0),Y (1))andT arein- Itis worthnoting thatour workisdomain-agnostic. However,
i i i
dependent. We experiment with two kinds of user profiles. The someattributes,suchashumidityandprecipitation,areclearlycor-
first is a vector of the frequencies of watched TV genres. This related. We posit that some domain knowledge could help us to
profile captures high-level genre preferences. The second profile refinethesetofinfluentialattributesandsubstantiallyimproveour
isestimatedfrommatrixM ∈ {0,1}nu×np thatindicateswhich results. Webelievethattheuncoveredweatherdependenciescan
programsarewatchedbywhichusers,wheren isthenumberof beincorporatedintoacontext-awarerecommendertoenhanceits
u
usersandn isthenumberofprograms. LetU ∈ Rnu×d bethe recommendations,andweleavethisforfuturework.
p
userlaterfactorsinrank-dtruncatedSVDofM. Thentheprofile
ofuseruistheu-throwofU.Wechoosed=16.Theeigenvalues References
ofM aresmallafterwards,andhencethefirst16singularvectors
[1] A.FarahatandM.C.Bailey. Howeffectiveistargetedadver-
ofM capturemostofitsstructure.
tising? InProceedingsofWWW,pages111–120,2012.
3.2 Results
[2] T.PartonenandJ.Lönnqvist. Seasonalaffectivedisorder. The
Fig.1(a)showsthenormalizedATTs(1)ofpressureon8popular Lancet,352(9137):1369–1374,1998.
genres.Weobservethatwhenthepressureislow,thefrequencyof
[3] J.Pearl. Causality. CambridgeUniversityPress,2009.
watching Dramas decreases by 5%. The same effect is observed
forbothuserprofiles,whichstrengthensthevalidityofourresults. [4] D.B.Rubin.Matchingtoremovebiasinobservationalstudies.
Fig.1(b)showthesignificanteffectsforallweather-genrecom- Biometrics,pages159–183,1973.
binations. WemeasurethesignificanceoftheATTbyitsestimate
dividedbyitsstandarderror,whichisitsstandarddeviation.There- [5] M. Xu, S. Berkovsky, S. Ardon, S. Triukose, A. Mahanti,
fore,thevalueof4(−4)meansthattheestimateis4standarddevi- and I. Koprinska. Catch-up TV recommendations: show old
ationabove(below)zero,andsignificantatp≈10−4.Weobserve favouritesandfindnewones. InProceedingsofRecSys,2013.