scholarly journals Sensitivity Analysis and Bounding of Causal Effects With Alternative Identifying Assumptions

2011 ◽  
Vol 36 (4) ◽  
pp. 415-440 ◽  
Author(s):  
Booil Jo ◽  
Amiram D. Vinokur
2014 ◽  
Vol 22 (2) ◽  
pp. 169-182 ◽  
Author(s):  
Matthew Blackwell

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.


2018 ◽  
Vol 12 (02) ◽  
pp. 215-235
Author(s):  
Yiping Yan ◽  
Wei Tu ◽  
Xu Ding ◽  
Lifeng Sun

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.


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