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references.bib
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@article{rosenbaumCentralRolePropensity1983,
title = {The central role of the propensity score in observational studies for causal effects},
author = {Rosenbaum, Paul R. and Rubin, Donald B.},
year = {1983},
month = {04},
date = {1983-04-01},
journal = {Biometrika},
pages = {41--55},
volume = {70},
number = {1},
doi = {10.1093/biomet/70.1.41},
url = {https://doi.org/10.1093/biomet/70.1.41},
langid = {en}
}
@article{hoMatchingNonparametricPreprocessing2007,
title = {Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference},
author = {Ho, Daniel E. and Imai, Kosuke and King, Gary and Stuart, Elizabeth A.},
year = {2007},
month = {06},
date = {2007-06-20},
journal = {Political Analysis},
pages = {199--236},
volume = {15},
number = {3},
doi = {10.1093/pan/mpl013},
url = {https://doi.org/10.1093/pan/mpl013},
langid = {en}
}
@article{kahanEliminatingAmbiguousTreatment2023,
title = {Eliminating Ambiguous Treatment Effects Using Estimands},
author = {Kahan, Brennan C and Cro, Suzie and Li, Fan and Harhay, Michael O},
year = {2023},
month = {02},
date = {2023-02-14},
journal = {American Journal of Epidemiology},
pages = {kwad036},
doi = {10.1093/aje/kwad036},
url = {https://academic.oup.com/aje/advance-article/doi/10.1093/aje/kwad036/7036583},
langid = {en}
}
@article{hanDefiningEstimandsClinical2023,
title = {Defining estimands in clinical trials: A unified procedure},
author = {Han, Shasha and Zhou, {Xiao{-}Hua}},
year = {2023},
month = {03},
date = {2023-03-08},
journal = {Statistics in Medicine},
pages = {sim.9702},
doi = {10.1002/sim.9702},
url = {https://onlinelibrary.wiley.com/doi/10.1002/sim.9702},
note = {tex.ids= hanDefiningEstimandsClinical
{\_}eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.9702},
langid = {en}
}
@article{greiferChoosingEstimandWhen2021,
title = {Choosing the Estimand When Matching or Weighting in Observational Studies},
author = {Greifer, Noah and Stuart, Elizabeth A.},
year = {2021},
month = {06},
date = {2021-06-19},
journal = {arXiv:2106.10577 [stat]},
url = {https://arxiv.org/abs/2106.10577},
note = {arXiv: 2106.10577}
}
@article{vanderweelePrinciplesConfounderSelection2019,
title = {Principles of confounder selection},
author = {VanderWeele, Tyler J.},
year = {2019},
month = {03},
date = {2019-03-01},
journal = {European Journal of Epidemiology},
pages = {211--219},
volume = {34},
number = {3},
doi = {10.1007/s10654-019-00494-6},
url = {https://doi.org/10.1007/s10654-019-00494-6},
langid = {en}
}
@article{kingDangersExtremeCounterfactuals2006,
title = {The dangers of extreme counterfactuals},
author = {King, Gary and Zeng, Langche},
year = {2006},
month = {03},
date = {2006-03-20},
journal = {Political Analysis},
pages = {131--159},
volume = {14},
number = {2},
doi = {10.1093/pan/mpj004},
url = {http://pan.oxfordjournals.org/content/14/2/131},
langid = {en}
}
@article{westreichInvitedCommentaryPositivity2010,
title = {Invited Commentary: Positivity in Practice},
author = {Westreich, Daniel and Cole, Stephen R.},
year = {2010},
month = {03},
date = {2010-03-15},
journal = {American Journal of Epidemiology},
pages = {674--677},
volume = {171},
number = {6},
doi = {10.1093/aje/kwp436},
url = {https://doi.org/10.1093/aje/kwp436}
}
@article{hernanDoesObesityShorten2008,
title = {Does obesity shorten life? The importance of well-defined interventions to answer causal questions},
author = {{Hernán}, Miguel A and Taubman, S L},
year = {2008},
month = {08},
date = {2008-08},
journal = {International Journal of Obesity},
pages = {S8--S14},
volume = {32},
number = {S3},
doi = {10.1038/ijo.2008.82},
url = {http://www.nature.com/articles/ijo200882},
langid = {en}
}
@article{coleConsistencyStatementCausal2009a,
title = {The Consistency Statement in Causal Inference: A Definition or an Assumption?},
author = {Cole, Stephen R. and Frangakis, Constantine E.},
year = {2009},
month = {01},
date = {2009-01},
journal = {Epidemiology},
pages = {3--5},
volume = {20},
number = {1},
doi = {10.1097/EDE.0b013e31818ef366},
url = {https://journals.lww.com/00001648-200901000-00003},
note = {tex.ids= coleConsistencyStatementCausal2009},
langid = {en}
}
@article{rosenbaumInterferenceUnitsRandomized2007,
title = {Interference Between Units in Randomized Experiments},
author = {Rosenbaum, Paul R},
year = {2007},
month = {03},
date = {2007-03},
journal = {Journal of the American Statistical Association},
pages = {191--200},
volume = {102},
number = {477},
doi = {10.1198/016214506000001112},
url = {http://www.tandfonline.com/doi/abs/10.1198/016214506000001112},
langid = {en}
}
@article{imaiCausalInferenceGeneral2004,
title = {Causal Inference with General Treatment Regimes: Generalizing the Propensity Score},
author = {Imai, Kosuke and Van Dyk, David A.},
year = {2004},
date = {2004},
journal = {Journal of the American Statistical Association},
pages = {854--866},
volume = {99},
number = {467},
url = {https://www.jstor.org/stable/27590455}
}
@article{tchetgenCausalInferencePresence2012,
title = {On causal inference in the presence of interference},
author = {Tchetgen, Eric J Tchetgen and VanderWeele, Tyler J},
year = {2012},
month = {02},
date = {2012-02-01},
journal = {Statistical Methods in Medical Research},
pages = {55--75},
volume = {21},
number = {1},
doi = {10.1177/0962280210386779},
url = {https://doi.org/10.1177/0962280210386779},
note = {Publisher: SAGE Publications Ltd STM},
langid = {en}
}
@article{fogartyDiscreteOptimizationInterpretable2016,
title = {Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality},
author = {Fogarty, Colin B. and Mikkelsen, Mark E. and Gaieski, David F. and Small, Dylan S.},
year = {2016},
month = {04},
date = {2016-04-02},
journal = {Journal of the American Statistical Association},
pages = {447--458},
volume = {111},
number = {514},
doi = {10.1080/01621459.2015.1112802},
url = {https://www.tandfonline.com/doi/full/10.1080/01621459.2015.1112802},
langid = {en}
}
@article{hernanEstimatingCausalEffects2006,
title = {Estimating causal effects from epidemiological data},
author = {{Hernán}, Miguel A and Robins, James M.},
year = {2006},
date = {2006},
journal = {Journal of Epidemiology and Community Health (1979-)},
pages = {578--586},
volume = {60},
number = {7},
url = {http://www.jstor.org/stable/40795098}
}
@article{brookhartConfoundingControlHealthcare2010a,
title = {Confounding Control in Healthcare Database Research: Challenges and Potential Approaches},
author = {Brookhart, M. Alan and {Stürmer}, Til and Glynn, Robert J. and Rassen, Jeremy and Schneeweiss, Sebastian},
year = {2010},
month = {06},
date = {2010-06},
journal = {Medical Care},
pages = {S114--S120},
volume = {48},
number = {6},
doi = {10.1097/MLR.0b013e3181dbebe3},
url = {https://journals.lww.com/00005650-201006001-00018},
note = {tex.ids= brookhartConfoundingControlHealthcare2010
PMCID: PMC4024462
PMID: 20473199},
langid = {en}
}
@article{greenlandCausalDiagramsEpidemiologic1999,
title = {Causal Diagrams for Epidemiologic Research},
author = {Greenland, Sander and Pearl, Judea and Robins, James M.},
year = {1999},
date = {1999},
journal = {Epidemiology},
pages = {37--48},
volume = {10},
number = {1},
url = {https://www.jstor.org/stable/3702180},
note = {Publisher: Lippincott Williams & Wilkins}
}
@article{matthayAlternativeCausalInference2020,
title = {Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence},
author = {Matthay, Ellicott C. and Hagan, Erin and Gottlieb, Laura M. and Tan, May Lynn and Vlahov, David and Adler, Nancy E. and Glymour, M. Maria},
year = {2020},
month = {04},
date = {2020-04-01},
journal = {SSM - Population Health},
pages = {100526},
volume = {10},
doi = {10.1016/j.ssmph.2019.100526},
url = {https://doi.org/10.1016/j.ssmph.2019.100526},
langid = {en}
}
@article{austinIntroductionPropensityScore2011,
title = {An introduction to propensity score methods for reducing the effects of confounding in observational studies},
author = {Austin, Peter C.},
year = {2011},
month = {05},
date = {2011-05-31},
journal = {Multivariate Behavioral Research},
pages = {399--424},
volume = {46},
number = {3},
doi = {10.1080/00273171.2011.568786},
url = {https://doi.org/10.1080/00273171.2011.568786},
langid = {en}
}
@article{harderPropensityScoreTechniques2010,
title = {Propensity score techniques and the assessment of measured covariate balance to test causal associations in psychological research},
author = {Harder, Valerie S. and Stuart, Elizabeth A. and Anthony, James C.},
year = {2010},
month = {09},
date = {2010-09},
journal = {Psychological Methods},
pages = {234--249},
volume = {15},
number = {3},
doi = {10.1037/a0019623},
url = {https://doi.org/10.1037/a0019623}
}
@article{caliendoPracticalGuidanceImplementation2008,
title = {Some Practical Guidance for the Implementation of Propensity Score Matching},
author = {Caliendo, Marco and Kopeinig, Sabine},
year = {2008},
month = {02},
date = {2008-02-01},
journal = {Journal of Economic Surveys},
pages = {31--72},
volume = {22},
number = {1},
doi = {10.1111/j.1467-6419.2007.00527.x},
url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1467-6419.2007.00527.x/abstract},
note = {{\_}eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-6419.2007.00527.x},
langid = {en}
}
@article{shadishPrimerPropensityScore2010,
title = {A Primer on Propensity Score Analysis},
author = {Shadish, William R. and Steiner, Peter M.},
year = {2010},
month = {03},
date = {2010-03-01},
journal = {Newborn and Infant Nursing Reviews},
pages = {19--26},
series = {Quantitative Research Methodology},
volume = {10},
number = {1},
doi = {10.1053/j.nainr.2009.12.010},
url = {http://www.sciencedirect.com/science/article/pii/S1527336909001780}
}
@article{benedettoStatisticalPrimerPropensity2018,
title = {Statistical primer: propensity score matching and its alternatives{\textdagger}},
author = {Benedetto, Umberto and Head, Stuart J and Angelini, Gianni D and Blackstone, Eugene H},
year = {2018},
month = {06},
date = {2018-06-01},
journal = {European Journal of Cardio-Thoracic Surgery},
pages = {1112--1117},
volume = {53},
number = {6},
doi = {10.1093/ejcts/ezy167},
url = {https://academic.oup.com/ejcts/article/53/6/1112/4978231},
note = {tex.ids= benedettoStatisticalPrimerPropensity},
langid = {en}
}
@article{elwertEndogenousSelectionBias2014,
title = {Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable},
author = {Elwert, Felix and Winship, Christopher},
year = {2014},
month = {07},
date = {2014-07-30},
journal = {Annual Review of Sociology},
pages = {31--53},
volume = {40},
number = {1},
doi = {10.1146/annurev-soc-071913-043455},
url = {https://doi.org/10.1146/annurev-soc-071913-043455},
langid = {en}
}
@article{hernanInstrumentsCausalInference2006,
title = {Instruments for Causal Inference: An Epidemiologist's Dream?},
author = {{Hernán}, Miguel A. and Robins, James M.},
year = {2006},
month = {07},
date = {2006-07},
journal = {Epidemiology},
pages = {360--372},
volume = {17},
number = {4},
doi = {10.1097/01.ede.0000222409.00878.37},
url = {https://journals.lww.com/00001648-200607000-00004},
langid = {en}
}
@article{greiferMatchingMethodsConfounder2021a,
title = {Matching Methods for Confounder Adjustment: An Addition to the Epidemiologist{\textquoteright}s Toolbox},
author = {Greifer, Noah and Stuart, Elizabeth A},
year = {2021},
month = {06},
date = {2021-06-10},
journal = {Epidemiologic Reviews},
pages = {mxab003},
doi = {10.1093/epirev/mxab003},
url = {https://doi.org/10.1093/epirev/mxab003},
note = {tex.ids= greiferMatchingMethodsConfounder2021}
}
@book{hernanCausalInferenceWhat2020,
title = {Causal Inference: What If},
author = {{Hernán}, Miguel A and Robins, James M},
year = {2020},
month = {12},
date = {2020-12-31},
publisher = {Chapman & Hall/CRC},
url = {https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1268/2020/01/ci_hernanrobins_21jan20.pdf},
address = {Boca Raton},
langid = {en}
}
@article{diamondGeneticMatchingEstimating2013,
title = {Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies},
author = {Diamond, Alexis and Sekhon, Jasjeet S.},
year = {2013},
date = {2013},
journal = {Review of Economics and Statistics},
pages = {932{\textendash}945},
volume = {95},
number = {3},
doi = {10.1162/REST_a_00318},
url = {http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00318},
langid = {en}
}
@article{austinOptimalFullMatching2015,
title = {Optimal full matching for survival outcomes: a method that merits more widespread use},
author = {Austin, Peter C. and Stuart, Elizabeth A.},
year = {2015},
date = {2015},
journal = {Statistics in Medicine},
pages = {3949--3967},
volume = {34},
number = {30},
doi = {10.1002/sim.6602},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.6602},
note = {{\_}eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.6602},
langid = {en}
}
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note = {tex.ids= viscontiHandlingLimitedOverlap2018},
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number = {4},
doi = {10.1177/0962280220983512},
url = {https://doi.org/10.1177/0962280220983512},
note = {tex.ids= liPropensityScoreAnalysis2021
publisher: SAGE Publications Ltd STM}
}
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url = {http://journals.sagepub.com/doi/10.3102/1076998609359785},
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url = {https://doi.org/10.1097/EDE.0000000000000595},
langid = {en}
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langid = {en}
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title = {Substantial Gains in Bias Reduction from Matching with a Variable Number of Controls},
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url = {http://doi.wiley.com/10.1111/j.0006-341X.2000.00118.x},
langid = {en}
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title = {Interval estimation for treatment effects using propensity score matching},
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note = {{\_}eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.2277},
langid = {en}
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title = {Large Sample Properties of Matching Estimators for Average Treatment Effects},
author = {Abadie, Alberto and Imbens, Guido W.},
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doi = {10.1111/j.1468-0262.2006.00655.x},
url = {https://doi.org/10.1111/j.1468-0262.2006.00655.x},
langid = {en}
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title = {Matching on the Estimated Propensity Score},
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url = {https://www.econometricsociety.org/doi/10.3982/ECTA11293},
langid = {en}
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langid = {en}
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title = {A comparison of 12 algorithms for matching on the propensity score},
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langid = {en}
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title = {Covariate Balance for Observational Effectiveness Studies: A Comparison of Matching and Weighting},
author = {Kush, Joseph M. and Pas, Elise T. and Musci, Rashelle J. and Bradshaw, Catherine P.},
year = {2022},
month = {09},
date = {2022-09-07},
journal = {Journal of Research on Educational Effectiveness},
pages = {1--24},
volume = {0},
number = {0},
doi = {10.1080/19345747.2022.2110545},
url = {https://doi.org/10.1080/19345747.2022.2110545},
note = {Publisher: Routledge
{\_}eprint: https://doi.org/10.1080/19345747.2022.2110545}
}
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title = {Why Propensity Scores Should Not Be Used for Matching},
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date = {2019-05-07},
journal = {Political Analysis},
pages = {1--20},
doi = {10.1017/pan.2019.11},
url = {https://doi.org/10.1017/pan.2019.11},
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title = {Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies},
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title = {Estimating the effect of treatment on binary outcomes using full matching on the propensity score},
author = {Austin, Peter C. and Stuart, Elizabeth A.},
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date = {2017-12},
journal = {Statistical Methods in Medical Research},
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volume = {26},
number = {6},
doi = {10.1177/0962280215601134},
url = {http://journals.sagepub.com/doi/10.1177/0962280215601134},
langid = {en}
}
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title = {The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes},
author = {Austin, Peter C. and Stuart, Elizabeth A.},
year = {2017},
month = {08},
date = {2017-08},
journal = {Statistical Methods in Medical Research},
pages = {1654--1670},
volume = {26},
number = {4},
doi = {10.1177/0962280215584401},
url = {http://journals.sagepub.com/doi/10.1177/0962280215584401},
langid = {en}
}
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title = {Generalized Full Matching},
author = {{Sävje}, Fredrik and Higgins, Michael J. and Sekhon, Jasjeet S.},
year = {2021},
month = {10},
date = {2021-10},
journal = {Political Analysis},
pages = {423--447},
volume = {29},
number = {4},
doi = {10.1017/pan.2020.32},
url = {https://doi.org/10.1017/pan.2020.32},
note = {Publisher: Cambridge University Press},
langid = {en}
}
@article{zubizarretaMatchingBalancePairing2014,
title = {Matching for Balance, Pairing for Heterogeneity in an Observational Study of the Effectiveness of for-Profit and Not-for-Profit High Schools in {{Chile}}},
author = {Zubizarreta, Jos{\'e} R. and Paredes, Ricardo D. and Rosenbaum, Paul R.},
year = {2014},
month = mar,
journal = {The Annals of Applied Statistics},
volume = {8},
number = {1},
pages = {204--231},
issn = {1932-6157},
doi = {10.1214/13-AOAS713},
urldate = {2017-06-13},
langid = {english},
file = {/Users/NoahGreifer/Zotero/storage/JACNSVBA/Zubizarreta et al. - 2014 - Matching for balance, pairing for heterogeneity in.pdf}
}
@article{cohnProfileMatchingGeneralization2021,
title = {Profile {{Matching}} for the {{Generalization}} and {{Personalization}} of {{Causal Inferences}}},
author = {Cohn, Eric R. and Zubizarreta, Jose R.},
year = {2021},
month = may,
journal = {arXiv:2105.10060 [stat]},
eprint = {2105.10060},
primaryclass = {stat},
urldate = {2021-10-22},
abstract = {We introduce profile matching, a multivariate matching method for randomized experiments and observational studies that finds the largest possible self-weighted samples across multiple treatment groups that are balanced relative to a covariate profile. This covariate profile can represent a specific population or a target individual, facilitating the tasks of generalization and personalization of causal inferences. For generalization, because the profile often amounts to summary statistics for a target population, profile matching does not require accessing individual-level data, which may be unavailable for confidentiality reasons. For personalization, the profile can characterize a single patient. Profile matching achieves covariate balance by construction, but unlike existing approaches to matching, it does not require specifying a matching ratio, as this is implicitly optimized for the data. The method can also be used for the selection of units for study follow-up, and it readily applies to multi-valued treatments with many treatment categories. We evaluate the performance of profile matching in a simulation study of generalization of a randomized trial to a target population. We further illustrate this method in an exploratory observational study of the relationship between opioid use treatment and mental health outcomes. We analyze these relationships for three covariate profiles representing: (i) sexual minorities, (ii) the Appalachian United States, and (iii) a hypothetical vulnerable patient. We provide R code with step-by-step explanations to implement the methods in the paper in the Supplementary Materials.},
archiveprefix = {arxiv},
langid = {english},
keywords = {Statistics - Methodology},
file = {/Users/NoahGreifer/Zotero/storage/PFFUW2AF/Cohn and Zubizarreta - 2021 - Profile Matching for the Generalization and Person.pdf;/Users/NoahGreifer/Zotero/storage/QX8CY5HV/Cohn and Zubizarreta - 2021 - Profile Matching for the Generalization and Person.pdf}
}
@article{niknamUsingCardinalityMatching2022,
title = {Using {{Cardinality Matching}} to {{Design Balanced}} and {{Representative Samples}} for {{Observational Studies}}},
author = {Niknam, Bijan A. and Zubizarreta, Jose R.},
year = {2022},
month = jan,
journal = {JAMA},
volume = {327},
number = {2},
pages = {173--174},
issn = {0098-7484},
doi = {10.1001/jama.2021.20555},
urldate = {2022-01-13},
abstract = {Cardinality matching is a computational method for finding the largest possible number of matched pairs of exposed and unexposed individuals from an observational data set, with specified patterns of baseline characteristics that represent a target population for analysis. In a report in JAMA Network Open, Benjet et al used cardinality matching to examine the association of neighborhood exposure to violence during an armed conflict in Nepal with the incidence of major depressive disorder in younger children and older individuals. The authors found that children younger than 11 years at the start of the conflict who were exposed to violence during the conflict were significantly more likely to develop major depressive disorder than matched children in unexposed neighborhoods. In contrast, there was no association between exposure to violence and development of depression among individuals aged 11 years or older.}
}
@article{fortinIndirectCovariateBalance2022,
ids = {fortinIndirectCovariateBalance},
title = {Indirect Covariate Balance and Residual Confounding: {{An}} Applied Comparison of Propensity Score Matching and Cardinality Matching},
shorttitle = {Indirect Covariate Balance and Residual Confounding},
author = {Fortin, Stephen P. and Schuemie, Martijn},
year = {2022},
month = dec,
journal = {Pharmacoepidemiology and Drug Safety},
volume = {31},
number = {12},
pages = {1242--1252},
issn = {1053-8569, 1099-1557},
doi = {10.1002/pds.5510},
urldate = {2023-05-31},
langid = {english},
keywords = {balance,cardinality matching,propensity score matching,residual confounding,systematic error},
file = {/Users/NoahGreifer/Zotero/storage/53QVNM8L/Fortin and Schuemie - Indirect covariate balance and residual confoundin.pdf}
}
@article{cohnProfileMatchingGeneralization2022,
title = {Profile Matching for the Generalization and Personalization of Causal Inferences},
author = {Cohn, Eric R. and Zubizarreta, {José R.}},
year = {2022},
month = {09},
date = {2022-09},
journal = {Epidemiology},
pages = {678},
volume = {33},
number = {5},
doi = {10.1097/EDE.0000000000001517},
url = {https://journals.lww.com/epidem/Fulltext/2022/09000/Profile_Matching_for_the_Generalization_and.11.aspx},
langid = {canadian}
}
@article{ben-michaelBalancingActCausal2021,
title = {The {{Balancing Act}} in {{Causal Inference}}},
author = {{Ben-Michael}, Eli and Feller, Avi and Hirshberg, David A. and Zubizarreta, Jos{\'e} R.},
year = {2021},
month = oct,
journal = {arXiv:2110.14831 [stat]},
eprint = {2110.14831},
primaryclass = {stat},
urldate = {2021-12-16},
abstract = {The idea of covariate balance is at the core of causal inference. Inverse propensity weights play a central role because they are the unique set of weights that balance the covariate distributions of different treatment groups. We discuss two broad approaches to estimating these weights: the more traditional one, which fits a propensity score model and then uses the reciprocal of the estimated propensity score to construct weights, and the balancing approach, which estimates the inverse propensity weights essentially by the method of moments, finding weights that achieve balance in the sample. We review ideas from the causal inference, sample surveys, and semiparametric estimation literatures, with particular attention to the role of balance as a sufficient condition for robust inference. We focus on the inverse propensity weighting and augmented inverse propensity weighting estimators for the average treatment effect given strong ignorability and consider generalizations for a broader class of problems including policy evaluation and the estimation of individualized treatment effects.},
archiveprefix = {arxiv},
keywords = {62Gxx,Statistics - Methodology},
file = {/Users/NoahGreifer/Zotero/storage/VXYPXF97/Ben-Michael et al. - 2021 - The Balancing Act in Causal Inference.pdf}
}
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title = {Propensity score weighting analysis and treatment effect discovery},
author = {Mao, Huzhang and Li, Liang and Greene, Tom},
year = {2018},
month = {06},
date = {2018-06-19},
journal = {Statistical Methods in Medical Research},
pages = {096228021878117},
doi = {10.1177/0962280218781171},
url = {http://journals.sagepub.com/doi/10.1177/0962280218781171},
langid = {en}
}
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title = {Diabetes medications and associations with Covid-19 outcomes in the N3C database: A national retrospective cohort study},
author = {Bramante, Carolyn T. and Johnson, Steven G. and Garcia, Victor and Evans, Michael D. and Harper, Jeremy and Wilkins, Kenneth J. and Huling, Jared D. and Mehta, Hemalkumar and Alexander, Caleb and Tronieri, Jena and Hong, Stephenie and Kahkoska, Anna and Alamgir, Joy and Koraishy, Farrukh and Hartman, Katrina and Yang, Kaifeng and Abrahamsen, Trine and {Stürmer}, Til and Buse, John B. and , },
editor = {Saokaew, Surasak},
year = {2022},
month = {11},
date = {2022-11-17},
journal = {PLOS ONE},
pages = {e0271574},
volume = {17},
number = {11},
doi = {10.1371/journal.pone.0271574},
url = {http://dx.doi.org/10.1371/journal.pone.0271574},
langid = {en}
}
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title = {Comparable safety profile between neuro-oncology procedures involving stereotactic needle biopsy (SNB) followed by laser interstitial thermal therapy (LITT) and LITT alone procedures},
author = {Sharma, Mayur and Do, Truong H. and Palzer, Elise F. and Huling, Jared D. and Chen, Clark C.},
year = {2023},
month = {03},