scholarly journals Improving causal inference with a doubly robust estimator that combines propensity score stratification and weighting

2017 ◽  
Vol 23 (4) ◽  
pp. 697-702 ◽  
Author(s):  
Ariel Linden
Keyword(s):  
Propensity Score ◽  
Causal Inference ◽  
Robust Estimator ◽  
Doubly Robust ◽  
Propensity Score Stratification

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 Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data

Biometrika ◽  
2009 ◽  
Vol 96 (3) ◽  
pp. 723-734 ◽  
Author(s):  
Weihua Cao ◽  
Anastasios A. Tsiatis ◽  
Marie Davidian
Keyword(s):  
Propensity Score ◽  
Incomplete Data ◽  
Propensity Scores ◽  
Robust Estimator ◽  
Robust Estimators ◽  
Population Mean ◽  
Recent Interest ◽  
Improved Performance ◽  
Doubly Robust

Abstract Considerable recent interest has focused on doubly robust estimators for a population mean response in the presence of incomplete data, which involve models for both the propensity score and the regression of outcome on covariates. The usual doubly robust estimator may yield severely biased inferences if neither of these models is correctly specified and can exhibit nonnegligible bias if the estimated propensity score is close to zero for some observations. We propose alternative doubly robust estimators that achieve comparable or improved performance relative to existing methods, even with some estimated propensity scores close to zero.

scholarly journals A Falsifiability Characterization of Double Robustness Through Logical Operators

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Constantine Frangakis
Keyword(s):  
Causal Inference ◽  
Robust Estimator ◽  
Missing Information ◽  
Logical Operators ◽  
Double Robustness ◽  
Inference Problems ◽  
Consistent Estimators ◽  
Doubly Robust ◽  
Semiparametric Theory

AbstractWe address the characterization of problems in which a consistent estimator exists in a union of two models, also termed as a doubly robust estimator. Such estimators are important in missing information, including causal inference problems. Existing characterizations, based on the semiparametric theory of projections, have seen sufficient progress, but can still leave one’s understanding less than satisfied as to when and especially why such estimation works. We explore here a different, explanatory characterization – an exegesis based on logical operators. We show that double robustness exists if and only if we can produce consistent estimators for each contributing model based on an “AND” estimator, i. e., an estimator whose consistency generally needs both models to be correct. We show how this characterization explains double robustness through falsifiability.

Demystifying a class of multiply robust estimators

Biometrika ◽  
2020 ◽  
Vol 107 (4) ◽  
pp. 919-933
Author(s):  
Wei Li ◽  
Yuwen Gu ◽  
Lan Liu
Keyword(s):  
Propensity Score ◽  
Parametric Models ◽  
Robust Estimator ◽  
Robust Estimators ◽  
Improve Model ◽  
Special Cases ◽  
Model Mixing ◽  
Doubly Robust ◽  
Multiple Robustness ◽  
Robust Procedures

Summary For estimating the population mean of a response variable subject to ignorable missingness, a new class of methods, called multiply robust procedures, has been proposed. The advantage of multiply robust procedures over the traditional doubly robust methods is that they permit the use of multiple candidate models for both the propensity score and the outcome regression, and they are consistent if any one of the multiple models is correctly specified, a property termed multiple robustness. This paper shows that, somewhat surprisingly, multiply robust estimators are special cases of doubly robust estimators, where the final propensity score and outcome regression models are certain combinations of the candidate models. To further improve model specifications in the doubly robust estimators, we adapt a model mixing procedure as an alternative method for combining multiple candidate models. We show that multiple robustness and asymptotic normality can also be achieved by our mixing-based doubly robust estimator. Moreover, our estimator and its theoretical properties are not confined to parametric models. Numerical examples demonstrate that the proposed estimator is comparable to and can even outperform existing multiply robust estimators.

scholarly journals Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference

2018 ◽  
Vol 38 (8) ◽  
pp. 1442-1458 ◽  
Author(s):  
Jing Qin ◽  
Tao Yu ◽  
Pengfei Li ◽  
Hao Liu ◽  
Baojiang Chen
Keyword(s):  
Missing Data ◽  
Propensity Score ◽  
Causal Inference ◽  
Single Index ◽  
Index Model ◽  
Single Index Model

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.

scholarly journals Entropy Balancing is Doubly Robust

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Qingyuan Zhao ◽  
Daniel Percival
Keyword(s):  
Propensity Score ◽  
Observational Studies ◽  
Optimization Problem ◽  
Entropy Maximization ◽  
Entropy Balancing ◽  
Covariate Balance ◽  
Original Proposal ◽  
Variance Bound ◽  
Doubly Robust ◽  
Theoretical Results

AbstractCovariate balance is a conventional key diagnostic for methods estimating causal effects from observational studies. Recently, there is an emerging interest in directly incorporating covariate balance in the estimation. We study a recently proposed entropy maximization method called Entropy Balancing (EB), which exactly matches the covariate moments for the different experimental groups in its optimization problem. We show EB is doubly robust with respect to linear outcome regression and logistic propensity score regression, and it reaches the asymptotic semiparametric variance bound when both regressions are correctly specified. This is surprising to us because there is no attempt to model the outcome or the treatment assignment in the original proposal of EB. Our theoretical results and simulations suggest that EB is a very appealing alternative to the conventional weighting estimators that estimate the propensity score by maximum likelihood.

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