Handbook of Matching and Weighting Adjustments for Causal Inference

634 Pages·2023·21.951 MB·English

by  José R. Zubizarreta, Elizabeth A. Stuart, Dylan S. Small, Paul R. Rosenbaum

Handbook of Matching and
Weighting Adjustments for
Causal Inference
An observational study infers the effects caused by a treatment, policy, program, in-
tervention, or exposure in a context in which randomized experimentation is unethi-
cal or impractical. One task in an observational study is to adjust for visible pretreat-
ment differences between the treated and control groups. Multivariate matching and
weighting are two modern forms of adjustment. This handbook provides a compre-
hensive survey of the most recent methods of adjustment by matching, weighting,
outcome modeling, and their combinations. Three additional chapters introduce
the steps from association to causation that follow after adjustments are complete. 
When used alone, matching and weighting do not use outcome information, so they are
part of the design of an observational study. When used in conjunction with models for
the outcome, matching and weighting may enhance the robustness of model-based ad-
justments. The book is for researchers in medicine, economics, public health, psychol-
ogy, epidemiology, public program evaluation, and statistics, who examine evidence of
the effects on human beings of treatments, policies, or exposures.
Chapman & Hall/CRC
Handbooks of Modern Statistical Methods
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Garrett Fitzmaurice, Department of Biostatistics, Harvard School of Public Health, Boston, MA, U.S.A.
The objective of the series is to provide high-quality volumes covering the state-of-the-art in the theory
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application through a synthesis of the key methodological developments and examples and case studies
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Handbook of Multiple Comparisons
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Handbook of Matching and Weighting Adjustments for Causal Inference
José Zubizarreta, Elizabeth A. Stuart, Dylan S. Small, Paul R. Rosenbaum
For more information about this series, please visit: https://www.crcpress.com/Chapman--
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Handbook of Matching and
Weighting Adjustments for
Causal Inference
Edited by
José R. Zubizarreta
Elizabeth A. Stuart
Dylan S. Small
Paul R. Rosenbaum
First edition published 2023
by CRC Press
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and by CRC Press
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© 2023 selection and editorial matter, José Zubizarreta, Elizabeth A. Stuart, Dylan S. Small, Paul R. Rosenbaum; indi-
vidual chapters, the contributors
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ISBN: 978-0-367-60952-8 (hbk)
ISBN: 978-0-367-60953-5 (pbk)
ISBN: 978-1-003-10267-0 (ebk)
DOI: 10.1201/9781003102670
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Contents
Contributors ix
AbouttheEditors xi
I ConceptualIssues 1
1 Overview of Methods for Adjustment and Applications in the Social and Behavioral
Sciences:TheRoleofStudyDesign 3
Ting-HsuanChangandElizabethA.Stuart
2 PropensityScore 21
PaulR.Rosenbaum
3 GeneralizabilityandTransportability 39
ElizabethTiptonandErinHartman
II Matching 61
4 OptimizationTechniquesinMultivariateMatching 63
PaulR.RosenbaumandJose´ R.Zubizarreta
5 OptimalFullMatching 87
MarkM.FredricksonandBenHansen
6 FineBalanceandItsVariationsinModernOptimalMatching 105
SamuelD.Pimentel
7 MatchingwithInstrumentalVariables 135
MikeBaiocchiandHyunseungKang
8 CovariateAdjustmentinRegressionDiscontinuityDesigns 153
MatiasD.Cattaneo,LukeKeele,Roc´ıoTitiunik
9 RiskSetMatching 169
BoLuandRobertA.Greevy,Jr.
10 MatchingwithMultilevelData 185
LukeKeeleandSamuelD.Pimentel
11 EffectModificationinObservationalStudies 205
KwonsangLee,JesseY.Hsu
12 OptimalNonbipartiteMatching 227
RobertA.Greevy,Jr.andBoLu
vii
viii Contents
13 MatchingMethodsforLargeObservationalStudies 239
RuoqiYu
III Weighting 261
14 OverlapWeighting 263
FanLi
15 CovariateBalancingPropensityScore 283
KosukeImaiandYangNing
16 BalancingWeightsforCausalInference 293
EricR.Cohn,EliBen-Michael,AviFeller,andJose´ R.Zubizarreta
17 AssessingPrincipalCausalEffectsUsingPrincipalScoreMethods 313
AlessandraMattei,LauraForastiere,FabriziaMealli
18 IncrementalCausalEffects:AnIntroductionandReview 349
MatteoBonvini,AlecMcCleanandZachBranson,EdwardH.Kennedy
19 WeightingEstimatorsforCausalMediation 373
DonnaL.Coffman,MeganS.Schuler,TrangQ.Nguyen,andDanielF.McCaffrey
IV OutcomeModels,MachineLearningandRelatedApproaches 413
20 MachineLearningforCausalInference 415
JenniferHill,GeorgePerrett,VincentDorie
21 TreatmentHeterogeneitywithSurvivalOutcomes 445
YizheXu,NikolaosIgnatiadis,ErikSverdrup,ScottFleming,StefanWager,NigamShah
22 WhyMachineLearningCannotIgnoreMaximumLikelihoodEstimation 483
MarkJ.vanderLaanandSherriRose
23 BayesianPropensityScoreMethodsandRelatedApproachesforConfoundingAdjust-
ment 501
JosephAntonelli
V BeyondAdjustments 529
24 HowtoBeaGoodCriticofanObservationalStudy 531
DylanS.Small
25 SensitivityAnalysis 553
C.B.Fogarty
26 EvidenceFactors 583
BikramKarmakar
Index 611
Contributors
JosephAntonelli ColinB.Fogarty
UniversityofFlorida UniversityofMichigan
Gainsville,FL AnnArbor,MI
MikeBaiocchi LauraForastiere
StanfordUniversity YaleUniversity
Stanford,CA NewHaven,CT
EliBen-Michael MarkFredrickson
UniversityofCalifornia UniversityofMichigan
Berkeley,CA AnnArbor,MI
MatteoBonvini
RobertGreevy,Jr.
CarnegieMellonUniversity
VanderbiltUniversity
Pittsburgh,PA
Nashville,TN
ZachBranson
BenB.Hansen
CarnegieMellonUniversity
UniversityofMichigan
Pittsburgh,PA
AnnArbor,MI
MatiasCattaneo
ErinHartman
PrincetonUniversity
UniversityofCalifornia
Princeton,NJ
Berkeley,CA
Ting-HsuanChang
JenniferHill
BloombergSchoolofPublicHealth,Johns
ColumbiaUniversity
HopkinsUniversity
NewYork,NY
Baltimore,MD
JesseHsu
DonnaCoffman
UniversityofPennsylvania
TempleUniversity
Philadelphia,PA
Philadelphia,PA
HyunseungKang
EricCohn
HarvardT.H.ChanSchoolofPublicHealth, UniversityofWisconsin
Madison,WI
HarvardUniversity
Boston,MA
BikramKarmakar
VincentDorie UniversityofFlorida
CodeforAmerica Gainsville,FL
AviFeller KosukeImai
UniversityofCalifornia HarvardUniversity
Berkeley,CA Cambridge,MA
ix