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Inverse probability of treatment weighting

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Published: 23 August 2017
Fig. 2 Inverse probability of treatment (IPT) weighted pathways linking socioeconomic disadvantage and child health to children’s cognitive achievement. D = socioeconomic disadvantage, H = child health, and O = cognitive achievement More
Journal Article
Demography (2023) 60 (6): 1767–1789.
Published: 01 December 2023
... composition, I use marginal structural models and inverse probability of treatment (IPT) weighting ( Robins et al. 2000 ). These models control for baseline and time-varying characteristics of children's households related to both changes in household composition and teen childbearing and address bias from...
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Includes: Supplementary data
Journal Article
Demography (2020) 57 (2): 501–528.
Published: 23 March 2020
... models and inverse probability of treatment weighting—methods that account for the fact that household composition is both a cause and consequence of other family characteristics—I find that doubling up shapes children’s life chances, but the effects vary depending on children’s relationships...
Includes: Supplementary data
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Published: 30 May 2013
Fig. 3 Stylized graph illustrating the effect of weighting by the inverse probability of treatment on the joint distribution of neighborhood poverty, time-varying covariates, and the outcome. A k = neighborhood poverty, L k = observed time-varying covariates, U k = unobserved More
Journal Article
Demography (2022) 59 (1): 293–320.
Published: 01 February 2022
... approaches for time-varying covariates can significantly bias estimates of focal relationships in an unknown direction and magnitude through overcontrolling and collider stratification ( Wodtke et al. 2011 ). Inverse probability of treatment (IPT)–weighted marginal structural models (MSMs) solve both...
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Includes: Supplementary data
Journal Article
Demography (2016) 53 (6): 1905–1932.
Published: 25 October 2016
... 2015 :153–168). Instead of regression adjustment, time-varying covariates L ¯ are controlled for by inverse probability treatment weighting in the final regression model. The overall weight for the grandchild i is w i = ∏ t = 1 T w i A t × ∏ t = 1 T w...
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Includes: Supplementary data
Journal Article
Demography (2022) 59 (1): 37–49.
Published: 01 February 2022
... inverse propensity weighting (IPW) to adjust the distribution of maternal characteristics so that pregnancy timing groups are similar with respect to observed variables that are (1) related to the probability of assignment to a pregnancy timing category and (2) influence the outcome of interest. The goal...
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Includes: Supplementary data
Journal Article
Demography (2017) 54 (3): 933–959.
Published: 05 June 2017
... on fertility in the short and medium term, up to five years after displacement. Our analysis is based on rich administrative data from Germany, with an observation period spanning more than 20 years. We apply inverse probability weighting (IPW) to flexibly control for the observed differences between women who...
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Includes: Supplementary data
Journal Article
Demography (2013) 50 (5): 1765–1788.
Published: 30 May 2013
...Fig. 3 Stylized graph illustrating the effect of weighting by the inverse probability of treatment on the joint distribution of neighborhood poverty, time-varying covariates, and the outcome. A k = neighborhood poverty, L k = observed time-varying covariates, U k = unobserved...
FIGURES | View All (4)
Includes: Supplementary data
Journal Article
Demography (2021) 58 (2): 773–784.
Published: 01 April 2021
... course. The endogeneity of neighborhood exposures is a key methodological issue in this literature, and the analysis in HHSP built on previous studies ( Sampson et al. 2008 ; Wodtke et al. 2011 ) that tackled this issue using marginal structural models with inverse probability of treatment weighting...
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Journal Article
Demography (2015) 52 (1): 83–111.
Published: 09 January 2015
.... These distributional differences motivated our efforts to separate the effects of pregnancy intentions on the outcomes from those attributable to the background characteristics. Next, we employed inverse propensity (probability) weights, an adaptation of propensity score analysis. Generally, propensity score...
Journal Article
Demography (2019) 56 (5): 1875–1897.
Published: 16 August 2019
... models in which the parameters are estimated using inverse probability of treatment (IPT) weights. This class of statistical methods reduces bias in estimators compared with conventional methods by adjusting for time-dependent confounding between family complexity and family economic resources...
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Includes: Supplementary data
Journal Article
Demography (2016) 53 (1): 85–116.
Published: 11 January 2016
..., it is not obvious how to derive ATE, TOT, or TOC estimates from such an analysis. Deriving these estimates is straightforward when using an inverse probability weighting (IPW) strategy described by Morgan and Todd ( 2008 ). Here the predicted probabilities of treatment are used to reweight the treated and control...
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Includes: Supplementary data
Journal Article
Demography (2021) 58 (6): 2041–2063.
Published: 01 December 2021
.... The foregoing results were robust to various specifications of the functional forms of the regressions used to estimate the inverse probability of treatment weights (see Eq. (6) in the technical online appendix). Models with nonlinear transformations and second-order terms on continuous variables, different...
Includes: Supplementary data
Journal Article
Demography (2017) 54 (5): 1845–1871.
Published: 23 August 2017
...Fig. 2 Inverse probability of treatment (IPT) weighted pathways linking socioeconomic disadvantage and child health to children’s cognitive achievement. D = socioeconomic disadvantage, H = child health, and O = cognitive achievement ...
FIGURES | View All (4)
Includes: Supplementary data
Journal Article
Demography (2019) 56 (1): 1–24.
Published: 05 December 2018
... might expect to see the fundamental cause perspective supported in an analysis of musculoskeletal pain but not of chronic conditions. We also take two analytical steps to address selectivity. One is the use of inverse probability of treatment weights (IPTWs), a statistical strategy through which we...
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Includes: Supplementary data
Journal Article
Demography (1987) 24 (3): 341–359.
Published: 01 August 1987
... investment than would be made if the parents had no concern about the distribution of qualities among their children). Among children within a family, if parental preferences are sufficiently sharply curved because of heavy weight on equity, therefore, schooling is inversely associated with expected child...
Journal Article
Demography (2018) 55 (2): 617–642.
Published: 14 March 2018
... for the subsidy, I use counterfactual causal methods to control for the selection of families into the subsidized housing program and for the duration and timing of receipt of subsidized housing. Specifically, I use inverse probability of treatment weighting (IPTW) to predict the receipt of subsidized housing...
Includes: Supplementary data
Journal Article
Demography (2019) 56 (2): 525–548.
Published: 16 January 2019
... to household instability. I use inverse probability of treatment (IPT) weighting and marginal structural models to reduce bias as a result of selection into household structure and changes. These models improve upon ordinary least squares models by addressing time-varying confounding variables...
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Includes: Supplementary data
Journal Article
Demography (2018) 55 (1): 1–31.
Published: 30 November 2017
... ) and showed that doing so is important. Both studies employed a propensity score technique for longitudinal data, known as a marginal structural model with inverse probability of treatment weighting (IPTW). As outlined later, however, this approach has several important disadvantages and limitations...
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Includes: Supplementary data