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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.