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.

scholarly journals Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies

2012 ◽  
Vol 20 (1) ◽  
pp. 25-46 ◽  
Author(s):  
Jens Hainmueller
Keyword(s):  
Monte Carlo ◽  
Propensity Score ◽  
Observational Studies ◽  
Causal Effects ◽  
Control Group ◽  
Higher Moments ◽  
Large Set ◽  
Entropy Balancing ◽  
Covariate Balance ◽  
And Control

This paper proposes entropy balancing, a data preprocessing method to achieve covariate balance in observational studies with binary treatments. Entropy balancing relies on a maximum entropy reweighting scheme that calibrates unit weights so that the reweighted treatment and control group satisfy a potentially large set of prespecified balance conditions that incorporate information about known sample moments. Entropy balancing thereby exactly adjusts inequalities in representation with respect to the first, second, and possibly higher moments of the covariate distributions. These balance improvements can reduce model dependence for the subsequent estimation of treatment effects. The method assures that balance improves on all covariate moments included in the reweighting. It also obviates the need for continual balance checking and iterative searching over propensity score models that may stochastically balance the covariate moments. We demonstrate the use of entropy balancing with Monte Carlo simulations and empirical applications.

Modern Algorithms for Matching in Observational Studies

2020 ◽  
Vol 7 (1) ◽  
pp. 143-176 ◽  
Author(s):  
Paul R. Rosenbaum
Keyword(s):  
Sensitivity Analysis ◽  
Propensity Score ◽  
Observational Studies ◽  
Propensity Scores ◽  
Variable Number ◽  
Exact Matching ◽  
Covariate Balance ◽  
Software Packages ◽  
Effective Use ◽  
Entire Number

Using a small example as an illustration, this article reviews multivariate matching from the perspective of a working scientist who wishes to make effective use of available methods. The several goals of multivariate matching are discussed. Matching tools are reviewed, including propensity scores, covariate distances, fine balance, and related methods such as near-fine and refined balance, exact and near-exact matching, tactics addressing missing covariate values, the entire number, and checks of covariate balance. Matching structures are described, such as matching with a variable number of controls, full matching, subset matching and risk-set matching. Software packages in R are described. A brief review is given of the theory underlying propensity scores and the associated sensitivity analysis concerning an unobserved covariate omitted from the propensity score.

scholarly journals Propensity score matching in otolaryngologic literature: A systematic review and critical appraisal

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244423
Author(s):  
Aman Prasad ◽  
Max Shin ◽  
Ryan M. Carey ◽  
Kevin Chorath ◽  
Harman Parhar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Observational Studies ◽  
Critical Appraisal ◽  
Propensity Scores ◽  
Final Analysis ◽  
Publication History ◽  
Covariate Balance

Background Propensity score techniques can reduce confounding and bias in observational studies. Such analyses are able to measure and balance pre-determined covariates between treated and untreated groups, leading to results that can approximate those generated by randomized prospective studies when such trials are not feasible. The most commonly used propensity score -based analytic technique is propensity score matching (PSM). Although PSM popularity has continued to increase in medical literature, improper methodology or methodological reporting may lead to biased interpretation of treatment effects or limited scientific reproducibility and generalizability. In this study, we aim to characterize and assess the quality of PSM methodology reporting in high-impact otolaryngologic literature. Methods PubMed and Embase based systematic review of the top 20 journals in otolaryngology, as measured by impact factor from the Journal Citations Reports from 2012 to 2018, for articles using PSM analysis throughout their publication history. Eligible articles were reviewed and assessed for quality and reporting of PSM methodology. Results Our search yielded 101 studies, of which 92 were eligible for final analysis and review. The proportion of studies utilizing PSM increased significantly over time (p < 0.001). Nearly all studies (96.7%, n = 89) specified the covariates used to calculate propensity scores. Covariate balance was illustrated in 67.4% (n = 62) of studies, most frequently through p-values. A minority (17.4%, n = 16) of studies were found to be fully reproducible according to previously established criteria. Conclusions While PSM analysis is becoming increasingly prevalent in otolaryngologic literature, the quality of PSM methodology reporting can be improved. We provide potential recommendations for authors regarding optimal reporting for analyses using PSM.

Multiple imputation analysis for propensity score matching with missing causes of failure: An application to hepatocellular carcinoma data

2021 ◽  
pp. 096228022110370
Author(s):  
Seungbong Han ◽  
Kam-Wah Tsui ◽  
Hui Zhang ◽  
Gi-Ae Kim ◽  
Young-Suk Lim ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Chronic Hepatitis ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Survival Data ◽  
Observational Studies ◽  
Imputation Method ◽  
Competing Risk ◽  
Data Imputation ◽  
Causes Of Failure

Propensity score matching is widely used to determine the effects of treatments in observational studies. Competing risk survival data are common to medical research. However, there is a paucity of propensity score matching studies related to competing risk survival data with missing causes of failure. In this study, we provide guidelines for estimating the treatment effect on the cumulative incidence function when using propensity score matching on competing risk survival data with missing causes of failure. We examined the performances of different methods for imputing the data with missing causes. We then evaluated the gain from the missing cause imputation in an extensive simulation study and applied the proposed data imputation method to the data from a study on the risk of hepatocellular carcinoma in patients with chronic hepatitis B and chronic hepatitis C.

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