目录 1.1 Individual casual effects 1.2 Average casual effects 1.5 Causation versus association Hern\(\'{a}\)n M. and Robins J. Causal Inference: What If. A: intervention, exposure, treatment consistency: \(Y=Y^A\) when A observed. 1.1 Individual casual…

目录 6.1 Causal diagrams 6.2 Causal diagrams and marginal independence 6.3 Causal diagrams and conditional independence 6.4 Positivity and consistency in causal diagrams 6.5 A structural classification of bias 6.6 The structure of effect modification F…

目录 4.1 Definition of effect modification 4.2 Stratification to identify effect modification 4.3 Why care about effect modification 4.4 Stratification as a form of adjustment 4.5 Matching as another form of adjustment 4.6 Effect modification and adjus…

Targeted learning methods build machine-learning-based estimators of parameters defined as features of the probability distribution of the data, while also providing influence-curve or bootstrap-based confidence internals. The theory offers a general…

目录 7.1 The structure of confounding Confounding and exchangeability Confounding and the backdoor criterion 7.4 Confounding and confounders 7.5 Single-world intervention graphs Confounding adjustment Fine Point The strength and direction of confoundin…

目录 概 2.1 Randomization 2.2 Conditional randomization 2.3 Standardization 2.4 Inverse probability weighting Technical Point 2.2 Formal definition of IP weights Technical Point 2.3 Equivalence of IP weighting and standardization Hern\(\'{a}\)n M. and R…

[统计]Causal Inference 原文传送门 http://www.stat.cmu.edu/~larry/=sml/Causation.pdf 过程 一.Prediction 和 causation 的区别 现实中遇到的很多问题实际上是因果问题,而不是预测. 因果问题分为两种:一种是 causal inference,比如给定两个变量 X.Y,希望找到一个衡量它们之间因果关系的参数 theta:另一种是 causal discovery,即给定一组变量,找到他们之间的因果关系.对于后面…

目录 Standardization 非参数情况 Censoring 参数模型 Time-varying 静态 IP weighting 无参数 Censoring 参数模型 censoring 条件下 V Time-varying G-estimation 非参数模型 参数模型 Time-varying Propensity Scores Instrumental Variables Binary Linear Setting Continuous Linear Setting Nonpara…

目录 22.1 The target trial 22.2 Causal effects in randomized trails 22.3 Causal effects in observational analyses that emulate a target trial 22.4 Time zero 22.5 A unified analysis for causal inference Fine Point Grace periods Technical Point Controlle…

目录 21.1 The g-formula for time-varying treatments 21.2 IP weighting for time-varying treatments 21.3 A doubly robust estimator for time-varying treatments 21.4 G-estimation for time-varying treatments 21.5 Censoring is a time-varying treatment Fine P…

目录 20.1 The elements of treatment-confounder feedback 20.2 The bias of traditional methods 20.3 Why traditional methods fail 20.4 Why traditional methods cannot be fixed 20.5 Adjusting for past treatment Fine Point Representing feedback cycles with a…

目录 14.1 The causal question revisited 14.2 Exchangeability revisited 14.3 Structural nested mean models 14.4 Rank preservation 14.5 G-estimation 14.6 Structural nested models with two or more parameters Fine Point Relation between marginal structural…

目录 9.1 Measurement Error The structure of measurement error 9.3 Mismeasured confounders 9.4 Intention-to-treat effect: the effect of a misclassified treatment 9.5 Per-protocol effect Fine Point The strength and direction of measurement bias Per-proto…

目录 8.1 The structure of selection bias 8.2 Examples of selection bias 8.3 Selection bias and confounding 8.4 Selection bias and censoring 8.5 How to adjust for selection bias 8.6 Selection without bias Fine Point Selection bias in case-control studie…

目录 5.1 Interaction requires a joint intervention 5.2 Identifying interaction 5.3 Counterfactual response types and interactions 5.4 Sufficient causes 5.5 Sufficient cause interaction 5.6 Counterfactual or sufficient-component causes? Fine Point More…

目录 概 3.1 3.2 Exchangeability 3.3 Positivity 3.4 Consistency First Second Fine Point 3.1 Identifiability of causal effects 3.2 Crossover randomized experiments 3.3 Possible worlds 3.4 Attributable fraction Technical Point 3.1 Positivity for standardiz…

翻译来自:http://news.csdn.net/article_preview.html?preview=1&reload=1&arcid=2825492 摘要:本文解释了回归分析及其优势,重点总结了应该掌握的线性回归.逻辑回归.多项式回归.逐步回归.岭回归.套索回归.ElasticNet回归等七种最常用的回归技术及其关键要素,最后介绍了选择正确的回归模型的关键因素. [编者按]回归分析是建模和分析数据的重要工具.本文解释了回归分析的内涵及其优势,重点总结了应该掌握的线性回归.逻辑回归…

为网页设计师和开发推荐20个令人敬畏的jQuery插件.例如滑块,图像画廊,幻灯片插件,jQuery的导航菜单,jQuery文件上传,图像旋转器,标签的插件,用户界面​​元素,网络接触形式,模态窗口,工具提示,字体大小,和许多其他真棒jQuery脚本收集在一个地方. jQuery OrgChart Plugin For Creating Visualising Organization Charts With jQuery jOrgChart jQuery的创建可视化组织结构图 jOrgChar…

[Topic Model]主题模型之概率潜在语义分析(Probabilistic Latent Semantic Analysis) 感觉LDA在实践中的优势其实不大,学好pLSA才是重点 阅读笔记 PLSI 2008年的时候,pLSA已经被新兴的LDA掩盖了. LDA是pLSA的generalization:LDA的hyperparameter设为特定值的时候,就specialize成pLSA了. 从工程应用价值的角度看,这个数学方法的generalization,允许我们用一个训练好的模型解…

COPY COPY has two forms: COPY [--chown=<user>:<group>] <src>... <dest> COPY [--chown=<user>:<group>] ["<src>",... "<dest>"] (this form is required for paths containing whitespace) Note: T…

上一篇总结了潜在语义分析(Latent Semantic Analysis, LSA),LSA主要使用了线性代数中奇异值分解的方法,但是并没有严格的概率推导,由于文本文档的维度往往很高,如果在主题聚类中单纯的使用奇异值分解计算复杂度会很高,使用概率推导可以使用一些优化迭代算法来求解. Thomas Hofmann 于1998年根据似然原理定义了生成模型并由此提出了概率潜在语义分析模型(Probabilistic Latent Semantic Analysis),简称PLSA. PLSA属于概率…

https://www.analyticsvidhya.com/blog/2015/08/comprehensive-guide-regression/ What is Regression Analysis? Why do we use Regression Analysis? What are the types of Regressions? Linear Regression Logistic Regression Polynomial Regression Stepwise Regre…

SYNOPSIS ALTER TABLE [ ONLY ] name [ * ] ADD [ COLUMN ] column type [ column_constraint [ ... ] ] ALTER TABLE [ ONLY ] name [ * ] DROP [ COLUMN ] column [ RESTRICT | CASCADE ] ALTER TABLE [ ONLY ] name [ * ] ALTER [ COLUMN ] column { SET DEFAULT expr…

1. Displaying Data with Core UI Elements (已看) 2. Responding to User Events for Interactive UIs (已看) 3. Inventory UIs (已看) 4. Playing and Manipulating Sounds 5. Creating Textures, Maps, and Materials 6. Shader Graphs and Video Players 7. Using Cameras…

[Topic Model]主题模型之概率潜在语义分析(Probabilistic Latent Semantic Analysis) 感觉LDA在实践中的优势其实不大,学好pLSA才是重点 阅读笔记 PLSI 2008年的时候,pLSA已经被新兴的LDA掩盖了. LDA是pLSA的generalization:LDA的hyperparameter设为特定值的时候,就specialize成pLSA了. 从工程应用价值的角度看,这个数学方法的generalization,允许我们用一个训练好的模型解…

流行病学研究常见的分析就是相关性分析了. 相关性分析某种程度上可以为我们提供一些研究思路,比如缺乏元素A与某种癌症相关,那么我们可以通过补充元素A来减少患癌率.这个结论的大前提是缺乏元素A会导致这种癌症,也就是说元素A和癌症有因果关系. 但实际上,元素A和癌症有相关性,不代表他们之间就有因果关系.也有可能是患癌症的人同时有其他的并发症,这种并发症会导致元素A缺乏. 再比如,研究表明,大胸女生与不爱运动相关.那么,到底是因为胸大的女性不爱运动,还是因为不爱运动导致胸大(肥胖). 如果不做其他分析,…

目录 概 主要内容 CDE NDE NIE TDE, TIE, PDE, PIE Judea Pearl. Direct and indirect effects. In Proceedings of the 17th conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., 2001. 概 CDE: Controlled Direct Effect; NDE: Natural D…

Click can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issue Authors: 王文杰,冯福利,何向南,张含望,蔡达成 SIGIR'21 新加坡国立大学,中国科学技术大学,南洋理工大学 论文链接:https://dl.acm.org/doi/pdf/10.1145/3404835.3462962 本文链接:https://www.cnblogs.com/zihaojun/p/15713705…

In this chapter, we would add background music to the game and play sound effect when the hero fires bullets. Because there’s so few codes to add that we could say a little more about audio engine here. Cocos2d-x has wrapped SimpleAudioEngine to cros…

Spring's AOP Framework Let's begin by looking at Spring's own AOP framework - a proxy-based framework that works in pure Java. You can use it in any application server and all the required classes are included in the Spring distribution. Although man…