PPT-Why causal answers are needed and how we can get them

Dr Steven Stillman Senior Fellow Motu Adjunct Professor of Economics Waikato NIDEA Theme Leader New Zealands individuals families and households Launch Symposium November 24 th 2010 The Importance of Experimental Evidence . Reversible Debugging. Ivan . Lanese. Focus research group. Computer Science . and Engineering Department. Univers. ity . of Bologna/INRIA. Bologna, Italy. Joint work with Elena Giachino (FOCUS) and Claudio Antares Mezzina (FBK Trento). 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. Michael Rosenblum. March 16, 2010. Overview. I describe the set of assumptions encoded by a causal directed acyclic graph (DAG). I use an example from page 15 of the book . Causality. by Judea Pearl (2009). . : A Ground-Breaking use of Directed Acyclic Graphs. Bob Stoddard SEMA. Mike Konrad. SEMA. Copyright 2015 Carnegie Mellon University. This . material is based upon work funded and supported by the Department of Defense under Contract No. FA8721-05-C-0003 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center.. Susan Athey, Stanford GSB. Based on joint work with Guido Imbens, Stefan Wager. References outside CS literature. Imbens and Rubin Causal Inference book (2015): synthesis of literature prior to big data/ML. theory . Sri Hermawati. The focus of this chapter is on the role of causal processes in decision making.. Newcombs . problem/. the predictors paradox. You are offered a choice between two boxes, B1 and B2. Box . Causes are . difference-makers. .. Effect need not be . universal/deterministic. .. N. ot . everyone who is bitten by a cobra . dies. .. N. ot . everyone who dies is bitten by a . cobra. .. B. ut . cobra bites still cause . . Richard Scheines. Philosophy, Machine Learning, . Human-Computer Interaction . Carnegie Mellon University. 2. Goals. Basic Familiarity with Causal Model Search: . What it is. What it can and cannot do. Not without controversy, however.. 8. Chi-square structural model minus chi-square measurement model with . df. (s)-. df. (m) degrees of freedom. . 9. Reliability (Really validity?). (∑. λ. ). 2. 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. Distributed Systems. Lecture 14. Michael Freedman. 2. Linearizability. Eventual. Consistency models. Sequential. Causal. Lamport. clocks: C(a) < C(z) Conclusion: . None. Vector clocks: V(a) < V(z) Conclusion: . 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. Applying Computational Causal Discovery in Biomedicine Greg Cooper, University of Pittsburgh Richard Scheines , Carnegie Mellon University 11/3/2018 Outline Motivation Basics of Causal Graphical Distributed Systems. Lecture . 16. Michael Freedman. 2. Linearizability. Eventual. Consistency models. Sequential. Causal. Lamport. clocks: C(a) < C(z) Conclusion: . None. Vector clocks: V(a) < V(z) Conclusion: .