Subject: Subject Sylbus: Introduction to Causal Inference - 097400

Introduction to Causal Inference - 097400
Given In
  Lecture Exercise Laboratory Project or
2 1     1

Determination of the grade according to progress during the semester and the submission of the final thesis

Prerequisites: Data Analysis 046193
or Machine Learning 2 097209
or ( Probability (Ie) 094411
and Foundations and Applications of Artificial Intelligence 096210 )
or ( Probability (Advanced) 094412
and Foundations and Applications of Artificial Intelligence 096210 )
or ( Probability (Ie) 094411
and Computational Learning Theory 236760 )
or ( Probability (Advanced) 094412
and Computational Learning Theory 236760 )

What Problems Require Causal Inference,Why Is It Harder Than Will Learn the Approaches of Pearl and Rubin, Including Causal Graphs. We Will Bring Examples from Medicine, Economics and Public Policy, Social Media, Marketing and Sales, and Public Health. Supervised at the End of the Course the Students Will Be Able to:
1. Identify Problems That Require Causal Inference Tools Not Sufficient for Causal Inference
3. Identify the Difference Between a Randomized Controlled Trial, Observational Study with No Hidden Confounding, and An Observational Study with Hidden Confounders
4. Define Sufficient Conditions for the Performance of Valid Causal Causality, Using Both the Language of Potential Outcomes and the Language of Causal Graphs
5. Use Covariate Adjustment, Matching, and Propensity Score Methods to Evaluate Causal Effects from Data
6. Draw a Causal Graph That Corresponds to a Given Data Generating Process
7. Identify Conditions in Which a Natural Experiment Takes Place, Particularly in Situations Where There Exists An Instrumental Variable

System of hours to the semesters
Semester Previous Semester information 01/2020 2020/2021 Winter Semester

2014cambridge university press,morgan, stephen l., and christopher winscounterfactuals and causal inference
2009cambridge university presspearl, judea.causality
2016john wiley and sonspearl, judea pearl, judea, madelyn glycausal inference in statistics: a primer.
2008princeton university pressangrist, joshua d., and j rn steffen pimostly harmless econometrics: an empiricist's comp
2015cambridge university pressimbens, guido w., and donald b. rubinausal inference in statistics, social, and biomedical sciences

Created in 08/03/2021 Time 13:28:19