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Avis et commentaires pour l'étudiant pour Inférence causale par Université Columbia

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À propos du cours

This course offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the way in which statisticians and applied researchers in many disciplines use data to make inferences about causal relationships. We will study methods for collecting data to estimate causal relationships. Students will learn how to distinguish between relationships that are causal and non-causal; this is not always obvious. We shall then study and evaluate the various methods students can use — such as matching, sub-classification on the propensity score, inverse probability of treatment weighting, and machine learning — to estimate a variety of effects — such as the average treatment effect and the effect of treatment on the treated. At the end, we discuss methods for evaluating some of the assumptions we have made, and we offer a look forward to the extensions we take up in the sequel to this course....
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par Agnes v B

Aug 04, 2019

par Seo-Woo C

May 15, 2019

par Byron S

Oct 30, 2018

par Max B

Nov 26, 2018

par Charles H

Dec 16, 2018

par Lucas B

Jun 06, 2019