PPT-Causal Clustering of Variables with Multiple Latent

Causes More Theory than Applied Peter Spirtes Erich Kummerfeld Richard Scheines Joe Ramsey 1 An example Person 1 Stress Depression 3 Religious Coping Task learn causal model. Roy Howell. Texas Tech University. Latent Variables, Constructs, and Constructions . Premise: It takes both conceptual clarity and representational accuracy to advance the process of knowledge accumulation.. from . Mass Cytometry Data. Presenters: . Ioannis Tsamardinos. and Sofia Triantafillou. Institute of Computer Science, Foundation for Research and Technology, Hellas. Computer Science Department, University of Crete. Part II: Definition and Properties. Nevin. L. Zhang. Dept. of Computer Science & Engineering. The Hong Kong Univ. of Sci. & Tech.. http://www.cse.ust.hk/~lzhang. AAAI 2014 Tutorial. Part II: Concept . Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Assoc. . Prof. Dr. Şehnaz . Şahinkarakaş. Introduction to Causal-Comparative Research. A . causal-comparative. . study. is. a . study in which the researcher attempts to determine the cause, or reason, for pre-existing differences in groups of . Naftali Weinberger. Tilburg Center for Logic, Ethics and Philosophy of Science. Time and Causality in the Sciences. June 8. th. , 2017. Principle of the . C. ommon Cause. iPad. Happiness. iPad. Happiness. Tony Cox. May 5, 2016. 1. Download free CAT software from: . http://cox-associates.com/CAT.htm. . Outline. Why CAT? Challenges for causal analytics. Ambiguous C-R associations: theory & practice. with the Max-Min Hill Climbing Algorithm. Konstantinos . Tsirlis. , Vincenzo . Lagani. , Sofia Triantafillou and . Ioannis. . Tsamardinos. Associate Professor. , Computer Science Department, University of Crete. Alan Nicewander. Pacific Metrics. Presented at a conference to honor . Dr. Michael W. Browne of the Ohio State University, September 9-10, 2010 . Using the factor analytic version of item response (IRT) models, . Causal arguments are inductive arguments in which the conclusion is a claim that one thing causes another.. For example:. Clogged arteries cause heart attacks. A rough surface produces friction. Exercise during heat causes sweating. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A tree-like diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Nevin. L. Zhang. Dept. of Computer Science & Engineering. The Hong Kong Univ. of Sci. & Tech.. http://www.cse.ust.hk/~lzhang. AAAI 2014 Tutorial. Part II: Concept . and Properties. Latent . Tree . Nisheeth. Coin toss example. Say you toss a coin N times. You want to figure out its bias. Bayesian approach. Find the generative model. Each toss ~ Bern(. θ. ). θ. ~ Beta(. α. ,. β. ). Draw the generative model in plate notation.