Basic Information
What Problems Require Causal Inference,why Is It Harder Than Supervised Learning, How Can One Learn Causal Relations From Data, We 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. Learning Outcomes# At The End of The Course The Students Will Be Able To# 1. Identify Problems That Require Causal Inference Tools 2. Explain Why and Under What Conditions Machine Learning Tools Are 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
Faculty: Data and Decision Sciences
|Undergraduate Studies
|Graduate Studies
Pre-required courses
Semestrial Information
Weekly Hours
2.5 Academic Credit • 2 Lecture Hours • 1 Discussion Hours
Responsible(s)
Uri Shalit
Registration Groups
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Weekly Hours
2.5 Academic Credit • 2 Lecture Hours • 1 Discussion Hours
Responsible(s)
Uri Shalit
Registration Groups
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Weekly Hours
2.5 Academic Credit • 2 Lecture Hours • 1 Discussion Hours
Responsible(s)
Rom Gutman
Registration Groups
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