scholarly journals The gap-closing estimand: A causal approach to study interventions that close disparities across social categories

2020 ◽  
Author(s):  
Ian Lundberg
Keyword(s):  
Social Stratification ◽  
Robust Estimator ◽  
Social Categories ◽  
Treatment Variable ◽  
Pay Gap ◽  
Causal Approach ◽  
Rigorous Study ◽  
Causal Framework ◽  
Doubly Robust ◽  
Gap Closing

Disparities across social categories such as race, gender, and class are central in social stratification. Questions about the manipulability of these constructs, however, hinder their placement within a causal framework. This paper sidesteps the manipulability problem by advancing a different causal question: what gap across categories would persist under an intervention to equalize a treatment variable? The proposal makes three contributions. First, I distinguish between a local intervention to randomly sampled units (empirically tractable) and a global intervention to the population (the less tractable target of prior research). Second, I formalize stochastic treatment assignment rules that can improve credibility. Third, I provide a doubly-robust estimator. I illustrate by examining the pay gap by class origins under an intervention to equalize class destinations. The paper concludes with implications for practice: gap-closing estimands provide tools for the rigorous study of inequality across social categories that could inform policies to close gaps.

scholarly journals The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories

2022 ◽  
pp. 004912412110557
Author(s):  
Ian Lundberg
Keyword(s):  
Open Source Software ◽  
Research Question ◽  
R Package ◽  
Causal Effects ◽  
Social Categories ◽  
Descriptive Research ◽  
Causal Approach ◽  
Causal Framework ◽  
Gap Closing

Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits. First, gap-closing estimands place categories like race in a causal framework without making them play the role of the treatment (which is philosophically fraught for non-manipulable variables). Second, gap-closing estimands empower researchers to study disparities using new statistical and machine learning estimators designed for causal effects. Third, gap-closing estimands can directly inform policy: if we sampled from the population and actually changed treatment assignments, how much could we close gaps in outcomes? I provide open-source software (the R package gapclosing) to support these methods.

scholarly journals Doubly Robust Estimation of Causal Effect

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Xiaochun Li ◽  
Changyu Shen
Keyword(s):  
Propensity Score ◽  
Causal Effect ◽  
Model Specification ◽  
Robust Estimator ◽  
Data Set ◽  
Multiple Regressions ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
Confounding Adjustment ◽  
The Relationship

Propensity score–based methods or multiple regressions of the outcome are often used for confounding adjustment in analysis of observational studies. In either approach, a model is needed: A model describing the relationship between the treatment assignment and covariates in the propensity score–based method or a model for the outcome and covariates in the multiple regressions. The 2 models are usually unknown to the investigators and must be estimated. The correct model specification, therefore, is essential for the validity of the final causal estimate. We describe in this article a doubly robust estimator which combines both models propitiously to offer analysts 2 chances for obtaining a valid causal estimate and demonstrate its use through a data set from the Lindner Center Study.

scholarly journals “Robust-Squared” Imputation Models Using Bart

2019 ◽  
Vol 7 (4) ◽  
pp. 465-497
Author(s):  
Yaoyuan V Tan ◽  
Carol A C Flannagan ◽  
Michael R Elliott
Keyword(s):  
Model Misspecification ◽  
Blood Alcohol Concentration ◽  
Alcohol Concentration ◽  
Penalized Splines ◽  
Robust Estimator ◽  
Robust Estimators ◽  
Sampling System ◽  
Additive Regression ◽  
Doubly Robust ◽  
Augmented Inverse Probability Weighting

Abstract Examples of “doubly robust” estimators for missing data include augmented inverse probability weighting (AIPWT) and penalized splines of propensity prediction (PSPP). Doubly robust estimators have the property that, if either the response propensity or the mean is modeled correctly, a consistent estimator of the population mean is obtained. However, doubly robust estimators can perform poorly when modest misspecification is present in both models. Here we consider extensions of the AIPWT and PSPP that use Bayesian additive regression trees (BART) to provide highly robust propensity and mean model estimation. We term these “robust-squared” in the sense that the propensity score, the means, or both can be estimated with minimal model misspecification, and applied to the doubly robust estimator. We consider their behavior via simulations where propensities and/or mean models are misspecified. We apply our proposed method to impute missing instantaneous velocity (delta-v) values from the 2014 National Automotive Sampling System Crashworthiness Data System dataset and missing Blood Alcohol Concentration values from the 2015 Fatality Analysis Reporting System dataset. We found that BART, applied to PSPP and AIPWT, provides a more robust estimate compared with PSPP and AIPWT.

scholarly journals Doubly robust estimator of risk in the presence of censoring dependent on time-varying covariates: application to a primary prevention trial for coronary events with pravastatin

2020 ◽  
Author(s):  
Takuya Kawahara ◽  
Tomohiro Shinozaki ◽  
Yutaka Matsuyama
Keyword(s):  
Model Misspecification ◽  
Clinical Trial Data ◽  
Dependent Censoring ◽  
Model Specification ◽  
Robust Estimator ◽  
Time Varying ◽  
Simulation Studies ◽  
Kaplan Meier ◽  
Doubly Robust ◽  
Time Varying Covariates

Abstract Background: In the presence of dependent censoring even after stratification of baseline covariates, the Kaplan–Meier estimator provides an inconsistent estimate of risk. To account for dependent censoring, time-varying covariates can be used along with two statistical methods: the inverse probability of censoring weighted (IPCW) Kaplan–Meier estimator and the parametric g-formula estimator. The consistency of the IPCW Kaplan–Meier estimator depends on the correctness of the model specification of censoring hazard, whereas that of the parametric g-formula estimator depends on the correctness of the models for event hazard and time-varying covariates. Methods: We combined the IPCW Kaplan–Meier estimator and the parametric g-formula estimator into a doubly robust estimator that can adjust for dependent censoring. The estimator is theoretically more robust to model misspecification than the IPCW Kaplan–Meier estimator and the parametric g-formula estimator. We conducted simulation studies with a time-varying covariate that affected both time-to-event and censoring under correct and incorrect models for censoring, event, and time-varying covariates. We applied our proposed estimator to a large clinical trial data with censoring before the end of follow-up. Results: Simulation studies demonstrated that our proposed estimator is doubly robust, namely it is consistent if either the model for the IPCW Kaplan–Meier estimator or the models for the parametric g-formula estimator, but not necessarily both, is correctly specified. Simulation studies and data application demonstrated that our estimator can be more efficient than the IPCW Kaplan–Meier estimator. Conclusions: The proposed estimator is useful for estimation of risk if censoring is affected by time-varying risk factors.

Living with diversity or living with difference? International perspectives on everyday perceptions of the social composition of diverse neighbourhoods

Divercities ◽  
2018 ◽  
pp. 211-234
Author(s):  
Katrin Großmann ◽  
Georgia Alexandri ◽  
Maria Budnik ◽  
Annegret Haase ◽  
Christian Haid ◽  
...  
Keyword(s):  
Social Environment ◽  
Social Stratification ◽  
Social Groups ◽  
Social Categories ◽  
Social Composition ◽  
International Perspectives ◽  
The Social ◽  
Neighbourhood Change ◽  
National Discourses

This chapter analyses which categories are mobilised by residents to describe the social groups in their area and which normative assessments are attached to those descriptions. This intersectionality approach allows one to see social stratification at work in how inhabitants of diverse neighbourhoods in Leipzig, Paris, and Athens perceive, describe, and judge their social environment. The three cities that are analysed represent different histories of diversification, and all three of them have experienced societal disruptions and change. The residents' own positionality shapes how they categorise other residents and judge their social environment. Moreover, the construction of social groups in diverse neighbourhoods in these cities draws on a variety of rather classic social categories and is influenced by national discourses. Stigmatisation often occurs at the intersections of these categories. Also, neighbourhood change is an important factor in the construction of social groups.

scholarly journals Data-Adaptive Bias-Reduced Doubly Robust Estimation

2016 ◽  
Vol 12 (1) ◽  
pp. 253-282 ◽  
Author(s):  
Karel Vermeulen ◽  
Stijn Vansteelandt
Keyword(s):  
Nuisance Parameter ◽  
Nuisance Parameters ◽  
Robust Estimator ◽  
Robust Estimators ◽  
Infinite Dimensional ◽  
Working Models ◽  
Finite Dimensional ◽  
Doubly Robust Estimation ◽  
Data Adaptive ◽  
Doubly Robust

Abstract Doubly robust estimators have now been proposed for a variety of target parameters in the causal inference and missing data literature. These consistently estimate the parameter of interest under a semiparametric model when one of two nuisance working models is correctly specified, regardless of which. The recently proposed bias-reduced doubly robust estimation procedure aims to partially retain this robustness in more realistic settings where both working models are misspecified. These so-called bias-reduced doubly robust estimators make use of special (finite-dimensional) nuisance parameter estimators that are designed to locally minimize the squared asymptotic bias of the doubly robust estimator in certain directions of these finite-dimensional nuisance parameters under misspecification of both parametric working models. In this article, we extend this idea to incorporate the use of data-adaptive estimators (infinite-dimensional nuisance parameters), by exploiting the bias reduction estimation principle in the direction of only one nuisance parameter. We additionally provide an asymptotic linearity theorem which gives the influence function of the proposed doubly robust estimator under correct specification of a parametric nuisance working model for the missingness mechanism/propensity score but a possibly misspecified (finite- or infinite-dimensional) outcome working model. Simulation studies confirm the desirable finite-sample performance of the proposed estimators relative to a variety of other doubly robust estimators.

Doubly robust estimation of attributable fractions in survival analysis

2014 ◽  
Vol 26 (2) ◽  
pp. 948-969 ◽  
Author(s):  
Arvid Sjölander ◽  
Stijn Vansteelandt
Keyword(s):  
Public Health ◽  
Health Impact ◽  
Attributable Fraction ◽  
Binary Outcomes ◽  
Robust Estimator ◽  
Likelihood Estimator ◽  
The Public ◽  
Attributable Fractions ◽  
Doubly Robust Estimation ◽  
Doubly Robust

The attributable fraction is a commonly used measure that quantifies the public health impact of an exposure on an outcome. It was originally defined for binary outcomes, but an extension has recently been proposed for right-censored survival time outcomes; the so-called attributable fraction function. A maximum likelihood estimator of the attributable fraction function has been developed, which requires a model for the outcome. In this paper, we derive a doubly robust estimator of the attributable fraction function. This estimator requires one model for the outcome, and one joint model for the exposure and censoring. The estimator is consistent if either model is correct, not necessarily both.

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