Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies
Open accessAbstract
The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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The absence of current smokers and women with no prior vaginal birth in the UP group could not have been resolved through IPTW.
“The absence of current smokers and women with no prior vaginal birth in the UP group could not have been resolved through IPTW 45”
From Hysterectomy versus uterine preservation for pelvic organ prolapse surgery: a prospective cohort study (2025) · cites this paper as [45]
The propensity score model was specified using variables known or hypothesized to be associated with both surgical group and outcomes or associated with outcomes only, but not those associated with surgical group only.
“The propensity score model was specified using variables that are known or hypothesized to be associated with both surgical group and outcomes or associated with outcomes only, but not those associated with surgical group only.45”
From Hysterectomy versus uterine preservation for pelvic organ prolapse surgery: a prospective cohort study (2025) · cites this paper as [45]
A sensitivity analysis with inverse probability of treatment weighting (IPTW) was conducted to reduce treatment selection bias and potential confounding factors.
“A sensitivity analysis with inverse probability of treatment weighting (IPTW) [19] of the patients' demographics and clinical characteristics was conducted to reduce treatment selection bias and potential confounding factors.”
From Composite Failure Associated With Sacrohysteropexy and Sacrocolpopexy With Supracervical Hysterectomy for Advanced Pelvic Organ Prolapse (2025) · cites this paper as [19]
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“A sensitivity analysis with inverse probability of treatment weighting (IPTW) [19] of the patients' demographics and clinical characteristics was conducted to reduce treatment selection bias and potential confounding factors.”
- Authors
- Austin, P. C., & Stuart, E. A.
- Affiliations
- University of Toronto (CA)
- Johns Hopkins University (US)
- Corresponding author
- Austin, Peter C.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2015-08-03
- Volume
- 34
- Issue
- 28
- Pages
- 3661-3679
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- PMC
- PMC4626409
- OpenAlex
- W2110818436
- Cited by
- 4,495
- References
- 60
- Citations by year
- 2026: 563 · 2025: 565 · 2024: 530 · 2023: 537 · 2022: 651 · 2021: 544 · 2020: 426 · 2019: 288 · 2018: 209 · 2017: 119 · 2016: 53 · 2015: 2 · 2013: 2
- FWCI
- 21.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Statistics in Medicine (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- (ICES)
- (MOHLTC)
- (CIHR) (86508, R01MH099010)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- inverse probability of treatment weighting, propensity score, causal treatment effects, observational studies, covariate balance, graphical method, best practices
- MeSH terms
- Humans, Monte Carlo Method, Models, Statistical, Outcome Assessment, Health Care, Propensity Score, Observational Studies as Topic
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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