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 Causal Subclassification Tree Algorithm and Robust Causal Effect Estimation via Subclassification

2020 ◽  
Vol 10 (1) ◽  
pp. 40
Author(s):  
Tomoshige Nakamura ◽  
Mihoko Minami
Keyword(s):  
Propensity Score ◽  
Propensity Scores ◽  
Causal Effect ◽  
Model Misspecification ◽  
Robust Estimator ◽  
Propensity Score Model ◽  
Confounding Variables ◽  
Score Model ◽  
Effect Estimation ◽  
Weighting Methods

In observational studies, the existence of confounding variables should be attended to, and propensity score weighting methods are often used to eliminate their e ects. Although many causal estimators have been proposed based on propensity scores, these estimators generally assume that the propensity scores are properly estimated. However, researchers have found that even a slight misspecification of the propensity score model can result in a bias of estimated treatment effects. Model misspecification problems may occur in practice, and hence, using a robust estimator for causal effect is recommended. One such estimator is a subclassification estimator. Wang, Zhang, Richardson, & Zhou (2020) presented the conditions necessary for subclassification estimators to have $\sqrt{N}$-consistency and to be asymptotically well-defined and suggested an idea how to construct subclasses.

Comparing Alternatives for Estimation from Nonprobability Samples

2019 ◽  
Vol 8 (2) ◽  
pp. 231-263 ◽  
Author(s):  
Richard Valliant
Keyword(s):  
Robust Estimation ◽  
Population Control ◽  
Simulation Studies ◽  
Data Set ◽  
External Data ◽  
Doubly Robust Estimation ◽  
Doubly Robust ◽  
External Population ◽  
Selection Probabilities ◽  
As If

Abstract Three approaches to estimation from nonprobability samples are quasi-randomization, superpopulation modeling, and doubly robust estimation. In the first, the sample is treated as if it were obtained via a probability mechanism, but unlike in probability sampling, that mechanism is unknown. Pseudo selection probabilities of being in the sample are estimated by using the sample in combination with some external data set that covers the desired population. In the superpopulation approach, observed values of analysis variables are treated as if they had been generated by some model. The model is estimated from the sample and, along with external population control data, is used to project the sample to the population. The specific techniques are the same or similar to ones commonly employed for estimation from probability samples and include binary regression, regression trees, and calibration. When quasi-randomization and superpopulation modeling are combined, this is referred to as doubly robust estimation. This article reviews some of the estimation options and compares them in a series of simulation studies.

On the causal effect of religiosity on life satisfaction using a propensity score matching technique

2016 ◽  
Vol 43 (10) ◽  
pp. 1031-1048 ◽  
Author(s):  
Roberto Zotti ◽  
Nino Speziale ◽  
Cristian Barra
Keyword(s):  
Life Satisfaction ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Religious Identity ◽  
Causal Effect ◽  
Religious Involvement ◽  
Well Being ◽  
Data Set ◽  
Content Type ◽  
Causal Impact

Purpose The purpose of this paper is to investigate the effect of religious involvement on subjective well-being (SWB), specifically taking into account the implication of selection effects explaining religious influence using the British Household Panel Survey data set. Design/methodology/approach In order to measure the level of religious involvement, the authors construct different indices on the base of individual religious belonging, participation and beliefs applying a propensity score matching estimator. Findings The results show that religious active participation plays a relevant role among the different aspects of religiosity; moreover, having a strong religious identity such as, at the same time, belonging to any religion, attending religious services once a week or more and believing that religion makes a great difference in life, has a high causal impact on SWB. The authors’ findings are robust to different aspects of life satisfaction. Originality/value The authors offer an econometric account of the causal impact of different aspects of religiosity finding evidence that the causal effect of religious involvement on SWB is better captured than through typical regression methodologies focussing on the mean effects of the explanatory variables.

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.

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.

A model averaging approach for estimating propensity scores by optimizing balance

2017 ◽  
Vol 28 (1) ◽  
pp. 84-101 ◽  
Author(s):  
Yuying Xie ◽  
Yeying Zhu ◽  
Cecilia A Cotton ◽  
Pan Wu
Keyword(s):  
Propensity Score ◽  
Exclusive Breastfeeding ◽  
Propensity Scores ◽  
Causal Effect ◽  
Model Averaging ◽  
Real Data ◽  
Parametric Modeling ◽  
Machine Learning Techniques ◽  
Mixed Feeding ◽  
Data Set

Many approaches, including traditional parametric modeling and machine learning techniques, have been proposed to estimate propensity scores. This paper describes a new model averaging approach to propensity score estimation in which parametric and nonparametric estimates are combined to achieve covariate balance. Simulation studies are conducted across different scenarios varying in the degree of interactions and nonlinearities in the treatment model. The results show that, based on inverse probability weighting, the proposed propensity score estimator produces less bias and smaller standard errors than existing approaches. They also show that a model averaging approach with the objective of minimizing the average Kolmogorov–Smirnov statistic leads to the best performing IPW estimator. The proposed approach is also applied to a real data set in evaluating the causal effect of formula or mixed feeding versus exclusive breastfeeding on a child’s body mass index Z-score at age 4. The data analysis shows that formula or mixed feeding is more likely to lead to obesity at age 4, compared to exclusive breastfeeding.

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.

scholarly journals Policy Learning With Observational Data

Econometrica ◽  
2021 ◽  
Vol 89 (1) ◽  
pp. 133-161 ◽  
Author(s):  
Susan Athey ◽  
Stefan Wager
Keyword(s):  
Observational Data ◽  
Functional Form ◽  
Causal Effect ◽  
Individual Characteristics ◽  
Efficient Estimation ◽  
Causal Effects ◽  
Robust Estimator ◽  
New Approach ◽  
Doubly Robust ◽  
Application Specific

In many areas, practitioners seek to use observational data to learn a treatment assignment policy that satisfies application‐specific constraints, such as budget, fairness, simplicity, or other functional form constraints. For example, policies may be restricted to take the form of decision trees based on a limited set of easily observable individual characteristics. We propose a new approach to this problem motivated by the theory of semiparametrically efficient estimation. Our method can be used to optimize either binary treatments or infinitesimal nudges to continuous treatments, and can leverage observational data where causal effects are identified using a variety of strategies, including selection on observables and instrumental variables. Given a doubly robust estimator of the causal effect of assigning everyone to treatment, we develop an algorithm for choosing whom to treat, and establish strong guarantees for the asymptotic utilitarian regret of the resulting policy.

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