PPT-Mesurer l’impact I: Inférence Causale et Méthodes Quasi-Expérimentales 

Alice Duhaut DIME Introduction Objectif principal Guider les décisions politiques et de programmes sur base des preuves solides Argument Plusieurs types dévidence du plus simple amp moinscouteux au plus complexe. Chris . Mathys. Wellcome Trust Centre for Neuroimaging. UCL. SPM Course (M/EEG). London, May 14, 2013. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. Rahul Sharma and Alex Aiken (Stanford University). 1. Randomized Search. x. = . i. ;. y = j;. while . y!=0 . do. . x = x-1;. . y = y-1;. if( . i. ==j ). assert x==0. No!. Yes!.  . 2. Invariants. MINERvA. Kevin McFarland. University of Rochester. NuINT11, Dehradun. 8 March 2011. ν. Goddess′. μ. To India . via. Indiana. Thank you to the organizers . for their efforts to allow me to attend . Kari Lock Morgan. Department of Statistical Science, Duke University. kari@stat.duke.edu. . with Robin Lock, Patti Frazer Lock, Eric Lock, Dennis Lock. ECOTS. 5/16/12. Hypothesis Testing:. Use a formula to calculate a test statistic. . True Experiments. Characteristics. Threats to validity controlled by experiments. Threats not controlled by experiments. Obstacles to true experiments in the field. . Quasi-experiments. The logic of quasi-experiments . Chris . Mathys. Wellcome Trust Centre for Neuroimaging. UCL. SPM Course. London, May 11, 2015. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. Yong Ma. Sparse model space for projected Hessian. Quasi-Newton FWI. Eigenvector (largest . eigenvalue. ). Eigenvector (smallest . eigenvalue. ). Sergio Pissanetzky. Sergio@SciControls.com. Emergent Inference. Any system. VISION. ROBOT. SOFTWARE. your mom. grab. an. object. computer. program. eyes. cameras,. sensors. translation. 100,000,000. Daniel R. Schlegel and Stuart C. Shapiro. Department of Computer Science and Engineering. University at Buffalo, The State University of New York. Buffalo, New York, USA. <. drschleg,shapiro. >@buffalo.edu. Warm up. Share your picture with the people at your table group.. Make sure you have your Science notebook, agenda and a sharpened pencil. use tape to put it in front of your table of contents. Describe the difference between observations and inferences. 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. An.  inference is an idea or conclusion that's drawn from evidence and reasoning. . An . inference.  is an educated . guess.. When reading a passage: 1) Note the facts presented to the reader and 2) use these facts to draw conclusions about . Warm up. Share your picture with the people at your table group.. Make sure you have your Science notebook, agenda and a sharpened pencil. use tape to put it in front of your table of contents. Describe the difference between observations and inferences. Based on the ITER-like and Snowflake divertor configuration of CFETR (Phase I, R = 5.7 m), SOLPS simulation is performed to understand the performance of radiative quasi-snowflake . divertor . with argon seeding..