Hierarchically Regularized Entropy Balancing

2021 ◽  
Author(s):  
Yiqing Xu ◽  
Eddie Yang
Keyword(s):  
Entropy Balancing

scholarly journals Nonparametric estimation of population average dose-response curves using entropy balancing weights for continuous exposures

2021 ◽  
Vol 21 (1) ◽  
pp. 69-110 ◽  
Author(s):  
Brian G. Vegetabile ◽  
Beth Ann Griffin ◽  
Donna L. Coffman ◽  
Matthew Cefalu ◽  
Michael W. Robbins ◽  
...  
Keyword(s):  
Nonparametric Estimation ◽  
Dose Response ◽  
Average Dose ◽  
Response Curves ◽  
Entropy Balancing ◽  
Population Average ◽  
Dose Response Curves

scholarly journals Transporting experimental results with entropy balancing

2021 ◽  
Author(s):  
Kevin P. Josey ◽  
Seth A. Berkowitz ◽  
Debashis Ghosh ◽  
Sridharan Raghavan
Keyword(s):  
Experimental Results ◽  
Entropy Balancing

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.

VP90 Which Matching Adjusted Indirect Comparison Method Is Best?

2019 ◽  
Vol 35 (S1) ◽  
pp. 94-95
Author(s):  
Jonathan Alsop ◽  
Lawrence Pont ◽  
Martin Scott
Keyword(s):  
Treatment Effectiveness ◽  
Indirect Comparison ◽  
Patient Characteristics ◽  
Comparison Method ◽  
Ease Of Use ◽  
Superior Performance ◽  
Weighting Method ◽  
Entropy Balancing ◽  
Level Data ◽  
Adjusted Indirect Comparison

IntroductionMatching adjusted indirect comparison (MAIC) methods are extremely useful when conducting ITCs, as they reduce baseline imbalances between studies, particularly upon patient characteristics that are confounded with treatment. The standard approach when conducting MAIC is that proposed by Signorovitch et al. (2010). However, there are newer, and potentially better, methods available.MethodsThree different MAIC methods (Signorovitch, Entropy Balancing, Polynomial Weighting) were compared using multiple phase 3 RCTs conducted in Diabetic Retinal Edema. The matching ability of each method was assessed, alongside its ability to avoid large weights (i.e. avoiding high leverage), and maximise effective same size (ESS). Each method's overall ease of use and impact upon estimates of treatment effectiveness were also evaluated.ResultsAll methods were able to precisely match the aggregate level data. However, the Entropy Balancing and Polynomial Weighting both outperformed the Signorovitch method in terms of having the lowest maximum weights. The Polynomial Weighting provided the highest ESS. The Entropy Balancing method was arguably the most challenging to implement, whilst the Signorovitch method the least. The Polynomial Weighting method appears to provide the greatest flexibility to the user.ConclusionsWhilst the Signorovitch method has become almost synonymous with MAIC, the Entropy Balancing and Polynomial Weighting methods offer potentially superior performance. In the absence of head-to-head trial data, these new MAIC approaches should provide less biased and more precise estimates of comparative effectiveness – ultimately leading to better decision making by regulators and payers.

scholarly journals Equivalence of entropy balancing and the method of moments for matching‐adjusted indirect comparison

2020 ◽  
Vol 11 (4) ◽  
pp. 568-572 ◽  
Author(s):  
David M. Phillippo ◽  
Sofia Dias ◽  
A. E. Ades ◽  
Nicky J. Welton
Keyword(s):  
Method Of Moments ◽  
Indirect Comparison ◽  
Entropy Balancing ◽  
Adjusted Indirect Comparison

scholarly journals Cardiovascular risks and bleeding with non-vitamin K antagonist oral anticoagulant versus warfarin in patients with type 2 diabetes: a tapered matching cohort study

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Dahai Yu ◽  
Zhanzheng Zhao ◽  
David Simmons
Keyword(s):  
Type 2 Diabetes ◽  
Vitamin K ◽  
Oral Anticoagulant ◽  
Large Scale ◽  
Vitamin K Antagonist ◽  
Exact Matching ◽  
Entropy Balancing ◽  
Risk Of Bleeding ◽  
Increased Risk

Abstract Background We compared the risk of bleeding and cardiovascular disease (CVD) events between non-vitamin K antagonist oral anticoagulant (NOAC) and warfarin in people with type 2 diabetes (T2DM). Methods 862 Incident NOAC users and 626 incident warfarin users with T2DM were identified from within 40 UK general practice (1/4/2017–30/9/2018). Outcomes included incident hospitalisation for bleeding, CVD and re-hospitalisation for CVD within 12 months since first anticoagulant prescription, identified from linked hospitalisation data. A tapered matching method was applied to form comparison cohorts: coarsened exact matching restricted the comparison to areas of sufficient overlap in missingness and characteristics: (i) demographic characteristics; (ii) clinical measurements; (iii) prior bleeding and CVD history; (iv) prescriptions with bleeding; (v) anti-hypertensive treatment(s); (vi) anti-diabetes treatment(s). Entropy balancing sequentially balanced NOAC and warfarin users on their distribution of (i–vi). Weighted logistic regression modelling estimated outcome odds ratios (ORs), using entropy balancing weights from steps i–vi. Results The 12-month ORs of bleeding with NOAC (n = 582) vs matched/balanced warfarin (n = 486) were 1.93 (95% confidence interval 0.97–3.84), 2.14 (1.03–4.44), 2.31 (1.10–4.85), 2.42 (1.14–5.14), 2.41 (1.12–5.18), and 2.51 (1.17–5.38) through steps i–vi. ORs for CVD re-hospitalisation was increased with NOAC treatment through steps i–vi: 2.21 (1.04–4.68), 2.13 (1.01–4.52), 2.47 (1.08–5.62), 2.46 (1.02–5.94), 2.51 (1.01–6.20), and 2.66 (1.02–6.94). Conclusions Incident NOAC use among T2DM is associated with increased risk of bleeding hospitalisation and CVD re-hospitalisation compared with incident warfarin use. For T2DM, caution is required in prescribing NOACs as first anticoagulant treatment. Further large-scale replication studies in external datasets are warranted.

scholarly journals Entropy Balancing for Continuous Treatments

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Stefan Tübbicke
Keyword(s):  
Mean Squared Error ◽  
Software Implementation ◽  
Causal Effects ◽  
Continuous Treatment ◽  
Higher Moments ◽  
Computationally Efficient ◽  
Treatment Variable ◽  
Entropy Balancing ◽  
Political Analysis ◽  
User Friendly

Abstract Interest in evaluating the effects of continuous treatments has been on the rise recently. To facilitate the estimation of causal effects in this setting, the present paper introduces entropy balancing for continuous treatments (EBCT) – an intuitive and user-friendly automated covariate balancing scheme – by extending the original entropy balancing methodology of Hainmueller, J. 2012. “Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies.” Political Analysis 20 (1): 25–46. In order to estimate balancing weights, the proposed approach solves a globally convex constrained optimization problem, allowing for computationally efficient software implementation. EBCT weights reliably eradicate Pearson correlations between covariates (and their transformations) and the continuous treatment variable. As uncorrelatedness may not be sufficient to guarantee consistent estimates of dose–response functions, EBCT also allows to render higher moments of the treatment variable uncorrelated with covariates to mitigate this issue. Empirical Monte-Carlo simulations suggest that treatment effect estimates using EBCT display favorable properties in terms of bias and root mean squared error, especially when balance on higher moments of the treatment variable is sought. These properties make EBCT an attractive method for the evaluation of continuous treatments. Software implementation is available for Stata and R.

scholarly journals Impacto de la suplementación con micronutrientes sobre la desnutrición crónica infantil en Perú

2020 ◽  
Vol 31 (3) ◽  
pp. 148-154
Author(s):  
Pedro Francke ◽  
Gustavo Acosta
Keyword(s):  
Machine Learning ◽  
Entropy Balancing ◽  
Para Determinar

La Desnutricion cronica infantil (DCI) condiciona el desarrollo fisico y mental de los nifios y nifias. A largo plazo, una alta incidencia puede generar y reforzar un circulo vicioso de desigualdad y pobreza. En ese sentido, evaluar el impacto de las intervenciones para reducir la DCI es importante para determinar si las politicas son efectivas o no. Objetivo: Evaluar el impacto de la suplementacion con micronutrientes sobre los niveles de desnutricion cronica infantil en el Peru en el periodo 2014-2017. Material y metodos: Se utilizo informacion de los nifios y nifias de 6 a 59 meses de edad de la Encuesta Demografica y de Salud Familiar (ENDES) de los afios del 2014 al 2017. Se aplico una estimacion cuantitativa de naturaleza econometrica que consta de dos pasos. Primero se balancea la muestra mediante dos metodologias: Entropy Balancing (EB) y Machine Learning (ML). Luego se realizan estimaciones de diferencias para dos variables, la probabilidad de sufrir DCI y el puntaje Z entre quienes sufren DCI. Resultados: El haber realizado algun consumo de micronutrientes incrementa la probabilidad de sufrir desnutricion cronica. Se encuentran efectos positivos sobre el puntaje Z en los nifios con DCI a partir de 54,1 sobres consumidos. Cuando se incluyen variables de control, los resultados no varian considerablemente. Conclusiones: La suplementacion con micronutrientes tiene efectos negativos en la reduccion de la DCI. Un impacto positivo solo se encuentra en los nifios con DCI y que consumen mas de 54,1 sobres; para cantidades menores, el consumo empeora los niveles de DCI.

Efectos del Programa de Adquisición de Alimentos (PAA) sobre los Apicultores del Estado de Ceará, Brasil: Un Abordaje por Entropy Balancing

2021 ◽  
Vol 39 (8) ◽  
Author(s):  
Diogo Brito Sobreira ◽  
Ahmad Saeed Khan ◽  
Patrícia Verônica Pinheiro Sales Lima
Keyword(s):  
Entropy Balancing

El Programa brasileño de Adquisición de Alimentos (PAA) compra productos de la agricultura familiar y los distribuye entre las personas socialmente vulnerables. Este trabajo busca evaluar los efectos del PAA sobre la producción y los ingresos de la actividad apícola en Ceará, Brasil. Se utilizó la metodología Entropy Balancing con regresiones OLS y se tuvieron en cuenta las características de los productores de miel participantes y no participantes del programa en 2011. Los resultados revelaron impactos positivos y significativos sobre los aspectos económicos para los beneficiarios del programa. Además, el total de colmenas pobladas es un mecanismo de transmisión importante para explicar estos efectos.

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