PPT-The Impact of Counterfactual

Thinking on Persuasion Kaiyu wang phd goodman school of business brock university Canada National university of Kaohsiung March 13 2014 What is Counterfactual Thinking C. Thinking. Negative Event. Enhances . positive mood. May allow for the development of new strategies for future use. What is CFT?. Thoughts about what . might have been; what could I have done . different (better). Responsibility. f. or Query Answers and non-Answers. Alexandra Meliou, Wolfgang . Gatterbauer. , . Katherine . Moore, . and . Dan . Suciu. http://db.cs.washington.edu/causality/. 1. Motivating . E. xample: Explanations. Bayes Net Perspectives on Causation and Causal Inference. 1. Example Problems. Genetic regulatory networks. Yeast – ~5000 genes, ~2,500,000 potential edges. 2. A gene regulatory network in mouse embryonic stem cells http://www.pnas.org/content/104/42/16438/F3.expansion.html. for control observations. More generally, one would also want to drop treatment observations if they have covariat University of Waikato. Possible Worlds. Day . 4. TOPICS. Lewis’ Counterfactual Theory of Causation. Counterfactual Conditionals. Causal Dependence. Testing the Counterexamples to Hume. Identity . proposes more holistic perspectives. What’s involved in “rigorous impact evaluation”?. . Presented by Jim Rugh. t. o NONIE Conference in Paris 28 March 2011 . Join me in a review the basics of:. Impoverished counterfactual thinking is associated with schizophrenia Christine Hooker; Neal J Roese; Sohee Park Psychiatry; Winter 2000; pg. 326 Reproduced with permission of the copyright owner. F Identify. cause - effect. 2. e. nable good policy decisions. 3. LET’S HAVE ICE CREAM. Confusing correlation with causation is easy and may provoke bad policy . decisions. 4. LET’S HAVE ICE CREAM. Class 5. Tony Cox. tcoxdenver@aol.com. . University of Colorado at Denver. Course web site: . http://cox-associates.com/6330/. . What is a predictive model?. “The probability that X will happen is p” is a predictive model. Models. and Impact Evaluation. Pasquale Lucio Scandizzo. University. of Rome «Tor Vergata. » and The World . Bank. The . three. . problems. . of impact . evaluation. (. Heckman. , 2010). P1. Evaluating . Paul Gertler. UC Berkeley. Note: slides by Sebastian Martinez, Christel Vermeersch and Paul Gertler. The content of this presentation reflects the views of the authors and not necessarily those of the World Bank. This version: November 2009.. Greg Lewis (MSR and NBER). Matt Taddy (MSR and Chicago). Goal. To work out how to use instrumental variables for counterfactual prediction using (arbitrary) machine learners. To explore the practicalities of implementing this approach using deep neural nets. Grammaticalization . and Reanalysis in Hebrew. Elior Elkayam. Tel Aviv University. The 5. th.  International Conference For Graduate Students On Diverse Approaches To Linguistics. 0. Introduction. Lecture 12: Causality 3. Guest lecturer: Tadeg . Quillien. School of Informatics, University of Edinburgh. Last week: causal inference. Oxygen. Wood. Spark. Fire. How can we discover the general causal relations among all these things?.