This is the GitHub organization (homepage) for MY457/557: Causal Inference for Observational and Experimental Studies at the London School of Economics.

The course website with the full syllabus, weekly readings, and links to relevant materials is here.

Course materials (slides and seminar materials) are directly available here, and are also linked through the website.

• Course convenor and lecturer: Dr. Daniel de Kadt, Methodology.
• Seminar leaders: Dr. Lanabi LaLova, Methodology, and Dr. Zach Dickson, Methodology.
• GTA (no office hours): Anton Könneke, Government.

Office hour may be booked via LSE's StudentHub. If you have questions or concerns about class material, problem sets, or the exam, please use the class forum on Moodle. We will generally not reply to emails about the course material, but we will reply promptly to questions posted on the forum. Of course, if you questions or concerns are of a private or personal nature, please email or attend office hours.

There is a reasonable amount of reading for this class, especially in the early weeks. You are strongly encouraged to do the reading before class, paying close attention to details (i.e., do not skim over equations). In addition to some key articles, throughout the term we will dip into three main textbooks, which we will refer to by their acronyms:

• MHE: Angrist and Pischke, Mostly Harmless Econometrics: An Empiricist's Companion, 2009, Princeton University Press.
• CIS: Imbens and Rubin, Causal Inference for Statistics, Social, and Biomedical Sciences, 2015, Cambridge University Press.
• TE Huntington-Klein, The Effect: An Introduction to Research Design and Causality, 2022, CRC Press.

Note: The three textbooks have very different flavours, and are pitched at different technical levels. MHE is the classic graduate-level text for causal inference, and is challenging but very accessible, though now a little out of date as it was published in 2009. CIS is very dense and very technical, and serves as a reference text for much of the foundational material in the class (weeks 1-5). TE is the most accessible textbook and is very applied, while being lighter on details and generally less technically focused. The reading list is designed to allow you pick your own adventure, to a degree.

If you are particularly interested in the course material, there will be additional readings set from the following textbooks (as well as a few articles):

• CMRI: Pearl, Causality: Models Reasoning and Inference (2nd Ed), 2009, Cambridge University Press.
• CISAP: Pearl, Glymour, and Jewell, Causal Inference in Statistics: A Primer, 2016, Wiley.
• CIWI: Hernan and Robins, Causal Inference: What If, 2020, Routledge.

Note: if you are particularly interested in graphical models and their application to causal inference, it is strongly recommended that you do all the readings from either CMRI or CISAP. CMRI is extremely technical and dense, while CISAP is a gentler (though not that gentle) introduction to some of the basics introduced in CMRI. If there are suggested readings from both books, you should choose either, not both.

Special thanks: This course has benefitted tremendously from materials made available by a number of outstanding scholars and teachers. I would especially like to thank Jens Hainmueller, Dominik Hangartner, and Teppei Yamamoto on whose materials this course draws heavily, and to Kiril Borusyak, Paul Goldsmith-Pinkham, and Peter Hull for their publicly available materials.