Model misspecification sensitivity analysis in estimating causal effects of interventions with non-compliance

2002 ◽  
Vol 21 (21) ◽  
pp. 3161-3181 ◽  
Author(s):  
Booil Jo
Keyword(s):  
Sensitivity Analysis ◽  
Model Misspecification ◽  
Causal Effects

scholarly journals A Selection Bias Approach to Sensitivity Analysis for Causal Effects

2014 ◽  
Vol 22 (2) ◽  
pp. 169-182 ◽  
Author(s):  
Matthew Blackwell
Keyword(s):  
Sensitivity Analysis ◽  
Causal Inference ◽  
Political Science ◽  
Selection Bias ◽  
Large Volume ◽  
Quantitative Approach ◽  
Causal Effects ◽  
Omitted Variables ◽  
Fundamental Fact ◽  
Unmeasured Confounders

The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This article combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alter their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.

Evaluating Actors’ Behavior on Social Media

2018 ◽  
Vol 12 (02) ◽  
pp. 215-235
Author(s):  
Yiping Yan ◽  
Wei Tu ◽  
Xu Ding ◽  
Lifeng Sun
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sensitivity Analysis ◽  
Causal Effects ◽  
Test Evaluation ◽  
Matching Method ◽  
Proposed Model ◽  
Promotion Effectiveness ◽  
Promotion Strategies ◽  
Baseline Covariates

While social networks have become primary promotion platforms for TV series, it is crucial to provide reliable measurements of promotion effectiveness for actors, which can guide them to select better promotion strategies when they post microblogs. In this paper, influence indexes are proposed to measure the influence of microblogs, and some measurements on actors’ microblogs also indicate and reveal some useful patterns in their promotion behaviors. Then, a propensity score-matching method is applied to these data to identify effective promotion strategies at two promotion periods. In experiments, the proposed model is shown to be significant by [Formula: see text]-test evaluation and the model is demonstrated to be adequately specified by balance diagnostics. We also do sensitivity analysis to analyze how hidden covariates impact conclusions. With this application of microblog data, the causal effects between promotion strategies and promotion results can be assessed. Finally, we treat not only baseline covariates, but also the promotion strategies as features and predict the influence of promotion microblogs. With these features, we can acquire a more accurate prediction effect.

High-dimensional generalized propensity score with application to omics data

2021 ◽  
Author(s):  
Qian Gao ◽  
Yu Zhang ◽  
Jie Liang ◽  
Hongwei Sun ◽  
Tong Wang
Keyword(s):  
Propensity Score ◽  
Model Misspecification ◽  
Brain Regions ◽  
Gene Expression Omnibus ◽  
Efficient Estimation ◽  
Adaptive Lasso ◽  
Causal Effects ◽  
High Dimensional ◽  
Omics Data ◽  
Pre Treatment

Abstract Propensity score (PS) methods are popular when estimating causal effects in non-randomized studies. Drawing causal conclusion relies on the unconfoundedness assumption. This assumption is untestable and is considered more plausible if a large number of pre-treatment covariates are included in the analysis. However, previous studies have shown that including unnecessary covariates into PS models can lead to bias and efficiency loss. With the ever-increasing amounts of available data, such as the omics data, there is often little prior knowledge of the exact set of important covariates. Therefore, variable selection for causal inference in high-dimensional settings has received considerable attention in recent years. However, recent studies have focused mainly on binary treatments. In this study, we considered continuous treatments and proposed the generalized outcome-adaptive LASSO (GOAL) to select covariates that can provide an unbiased and statistically efficient estimation. Simulation studies showed that when the outcome model was linear, the GOAL selected almost all true confounders and predictors of outcome and excluded other covariates. The accuracy and precision of the estimates were close to ideal. Furthermore, the GOAL is robust to model misspecification. We applied the GOAL to seven DNA methylation datasets from the Gene Expression Omnibus database, which covered four brain regions, to estimate the causal effects of epigenetic aging acceleration on the incidence of Alzheimer’s disease.

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