In many empirical studies, researchers seek to estimate causal relationships using instrumental variables. When few valid instruments are available, researchers typically estimate models that impose a linear relationship between the dependent variable and the endogenous variable, even when the true model is likely to be non-linear. In the presence of non-linearity, ordinary least squares (OLS) ...

Abstract Investigating the causal relationship between exposure and time-to-event outcome is an important topic in biomedical research. Previous literature has discussed potential issues of using hazard ratio (HR) as marginal effect measure due to noncollapsibility. In this article, we advocate restricted mean survival time (RMST) difference a measure, which collapsible simple interpretation ar...

The relationship between occlusal overload and peri-implant bone loss remains a controversial topic in implant dentistry. A causal relationship between the incidence of marginal bone loss next to an implant and occlusal overload implies a treatment plan and occlusal scheme would benefit from a force management approach. A MEDLINE-assisted and hand search of peer-reviewed English literature and ...

Using the short-term multi-sector econometric model, this paper discusses in detail the multiplier effect of public investment in Japan. After examining its fundamental characteristics, the differ ence in government investment multiplier by the pattern of investment distribution among indus tries and the factors causing such a difference are analyzed. According to the simulation results, the mu...

Due to the need to evaluate the effectiveness of community-based programs in practice, there is substantial interest in methods to estimate the causal effects of community-level treatments or exposures on individual level outcomes. The challenge one is confronted with is that different communities have different environmental factors affecting the individual outcomes, and all individuals in a c...

According to the authors, time-modified confounding occurs when the causal relation between a time-fixed or time-varying confounder and the treatment or outcome changes over time. A key difference between previously described time-varying confounding and the proposed time-modified confounding is that, in the former, the values of the confounding variable change over time while, in the latter, t...

We present Bayesian models for finding the longitudinal causal effects of a randomized two-arm training program when compliance with the randomized assignment is less than perfect in the training arm (but perfect in the non-training arm) for reasons that are potentially correlated with the outcomes. We deal with the latter confounding problem under the principal stratification framework of Somm...

Empirical researchers commonly use instrumental variable (IV) assumptions to identify treatment effects. However, the credibility of these assumptions are often questionable. In this paper, we consider what can be learned when the assumptions necessary for point identification are violated in two specific ways. First, we allow the data to be contaminated, meaning that the exclusion restrictions...