Nested doubly robust estimating equations for causal analysis with an incomplete effect modifier

2021 ◽  
Author(s):  
Liqun Diao ◽  
Richard J. Cook
Keyword(s):  
Estimating Equations ◽  
Causal Analysis ◽  
Effect Modifier ◽  
Doubly Robust

Doubly Robust and Multiple-Imputation-Based Generalized Estimating Equations

2011 ◽  
Vol 21 (2) ◽  
pp. 202-225 ◽  
Author(s):  
Teshome Birhanu ◽  
Geert Molenberghs ◽  
Cristina Sotto ◽  
Michael G. Kenward
Keyword(s):  
Multiple Imputation ◽  
Generalized Estimating Equations ◽  
Estimating Equations ◽  
Doubly Robust ◽  
Generalized Estimating

scholarly journals Doubly robust adaptive LASSO for effect modifier discovery

2022 ◽  
Vol 0 (0) ◽  
Author(s):  
Asma Bahamyirou ◽  
Mireille E. Schnitzer ◽  
Edward H. Kennedy ◽  
Lucie Blais ◽  
Yi Yang
Keyword(s):  
Structural Model ◽  
Inhaled Corticosteroids ◽  
Adaptive Lasso ◽  
Marginal Structural Model ◽  
Treatment Variable ◽  
Effect Modifier ◽  
Robust Adaptive ◽  
Pre Treatment ◽  
Using Data ◽  
Doubly Robust

Abstract Effect modification occurs when the effect of a treatment on an outcome differsaccording to the level of some pre-treatment variable (the effect modifier). Assessing an effect modifier is not a straight-forward task even for a subject matter expert. In this paper, we propose a two-stageprocedure to automatically selecteffect modifying variables in a Marginal Structural Model (MSM) with a single time point exposure based on the two nuisance quantities (the conditionaloutcome expectation and propensity score). We highlight the performance of our proposal in a simulation study. Finally, to illustrate tractability of our proposed methods, we apply them to analyze a set of pregnancy data. We estimate the conditional expected difference in the counterfactual birth weight if all women were exposed to inhaled corticosteroids during pregnancy versus the counterfactual birthweight if all women were not, using data from asthma medications during pregnancy.

The Impact of Business Mentoring on Micro, Small, Medium Enterprises Performance

2017 ◽  
Vol 1 (1) ◽  
pp. 31-37
Author(s):  
Mohammad Adrian ◽  
Hendrati Dwi Mulyaningsih ◽  
Santi Rahmawati
Keyword(s):  
Quantitative Research ◽  
Causal Analysis ◽  
Coefficient Of Determination ◽  
Mentoring Program ◽  
Simple Regression Analysis ◽  
Small Medium Enterprises ◽  
Medium Enterprises ◽  
The Impact ◽  
Necessity Entrepreneur ◽  
Simple Regression

This reasearch is conducted on MMSME (Micro Small Medium Enterprises) that are participated in the MMSME Syari’ah Mentoring Program by Academicians and Practitioners (PUSPA) organized by Bank Indonesia in Bandung. MMSME who participated in PUSPA program 2016 is MMSME that included in necessity entrepreneur where MMSME operated just to fullfil the life necessities. The purpose of this reasearch was to investigate the influence of the business mentoring on the MMSME performance in PUSPA program 2016. Researcher used quantitative research method. Data were analyzed using simple regression analysis and descriptive-causal analysis. The result showed that business mentoring affect the performance of MMSME that participated in PUSPA Program 2016. Based on the calculation, coefficient of determination (R2) can be seen the influence of business mentoring variable (X) on the performance (Y) is 74%. While the remaining 26% is influenced by other factors such as entrepreneurship competence and human resources.

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