講座題目：Introducing the specificity score: a measure of causality beyond P value
主 講 人：北京大學(xué)苗旺研究員
講座時(shí)間：2023年6月28日（周三）10：00-11：00
講座地點(diǎn)：6號(hào)學(xué)院樓402會(huì)議室
主辦單位：新葡萄8883官網(wǎng)AMG浙江省2011“數(shù)據(jù)科學(xué)與大數(shù)據(jù)分析協(xié)同創(chuàng)新中心”
摘 要：
There is considerable debate and doubt about the use of P value in scientific research in recent years, particularly after its use is banished in several prestigious journals. Much scientific research is concerned with uncovering causal associations. However, P value is mostly a measure of the significance of a statistical association, which could be biased from the causal association of interest and lead to false/trivial scientific discoveries particularly in the presence of unmeasured confounding. In this talk, I will introduce 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. Under certain conditions, this approach has controlled type I error and power approaching unity for testing the null hypothesis of no causal effect. A visualization approach using a heatmap of specificity is proposed to communicate all specificity score/test information in a universal and effective manner. This approach only entails a rough idea on the broadness of the causal associations in sight, e.g., the maximum or upper-bound number of causes/outcomes of an outcome/treatment, but does not require to know exactly the exclusion of certain causal effects or the availability of auxiliary variables. This approach is related to Hill's specificity criterion for causal inference, but I will discuss the difference from Hill's. This approach admits for joint causal discovery with multiple treatments and multiple outcomes, which is particularly suitable for gene expressions studies, Mendelian randomization and EHR studies. 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.
主講人簡(jiǎn)介：
苗旺，現(xiàn)為北京大學(xué)概率統(tǒng)計(jì)系研究員，2008-2017年在北京大學(xué)數(shù)學(xué)科學(xué)學(xué)院讀本科和博士，2017-2018年在哈佛大學(xué)生物統(tǒng)計(jì)系做博士后研究，2018年入職北京大學(xué)。主要研究興趣包括因果推斷，缺失數(shù)據(jù)，半?yún)?shù)統(tǒng)計(jì)，及其在生物統(tǒng)計(jì)，流行病學(xué)，經(jīng)濟(jì)學(xué)和人工智能研究中的應(yīng)用，與合作者提出混雜分析的代理推斷理論，發(fā)展非隨機(jī)缺失數(shù)據(jù)的識(shí)別性和雙穩(wěn)健估計(jì)理論，以及數(shù)據(jù)融合的半?yún)?shù)理論，獲得國(guó)家重點(diǎn)研發(fā)計(jì)劃青年科學(xué)家項(xiàng)目和國(guó)家自然科學(xué)基金面上項(xiàng)目資助。擔(dān)任中國(guó)現(xiàn)場(chǎng)統(tǒng)計(jì)研究會(huì)因果推斷分會(huì)常務(wù)副理事長(zhǎng)。個(gè)人網(wǎng)頁(yè)https://www.math.pku.edu.cn/teachers/mwfy。
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