A1127
Title: Introducing the specificity score: A measure of causality beyond P value
Authors: Wang Miao - Peking University (China) [presenting]
Abstract: There has been considerable doubt and debate about using the P value in scientific research in recent years, particularly after its use was banned in several prestigious journals. Much scientific research is concerned with uncovering causal associations. However, the P value, by definition, is a measure of the significance of a statistical association, which could be biased from the causal association of interest and lead to false discoveries due to confounding. A score measuring the specificity of causal associations and a specificity score-based test about the existence of causal effects in the presence of unmeasured confounding will be introduced. Under certain conditions, this approach has controlled type I error and power approaching unity for testing the null hypothesis of no causal effect. This approach is particularly suitable for joint causal discovery with multiple treatments and multiple outcomes, such as gene expression studies, Mendelian randomization and EHR studies. A visualization approach using a specificity map is proposed to communicate all specificity score/test information in a universal and effective manner. Identification and estimation will be briefly covered. Simulations are used for illustration, and an application to a mouse obesity dataset detects potential active effects of genes on clinical traits that are relevant to metabolic syndrome.
