Causal Inference and Bayesian Analysis

Content
This course is for PhD students and researchers who are curious about quantitative analysis. We will focus on causal inference and Bayesian analysis. We do not expect you to have any statistics background (likely a good thing).
The course contains:

• Descriptive and inferential statistical techniques.

• Causal analysis.

• Usage of statistical tools.

It is a very hands-on course, which will allow you to up your skills in data analysis.

We will meet on Fridays 13.00-15.00, Sept 12 to Dec 19, i.e., 15 occasions. 

Before we meet, we expect participants to have read the relevant chapter(s) in the course book and watched accompanying videos. It is also good if you keep an eye on the lecture notes and exercises for each week.

Preliminary schedule.

Date	Title	Material*	Teacher
Sept 12	Introduction	E1, LN1, CH1-3	Richard
Sept 19	Design of models I	E2, LN2, CH4-6	Richard
Sept 26	Design of models II	CH4-6 (focus on DAGs)	Julian
Oct 3	Information theory and maximum entropy	E3, LN3.1, LN3, CH7	Richard
Oct 10	Interactions and sampling	E4, LN4, CH8-9	Richard
Oct 17	GLMs	E5, LN5, CH10-11	Richard
Oct 24	Funky distributions	E6, LN6, CH12	Richard
Oct 31	Multilevel models	E7, LN7, CH13	Richard
Nov 7	Covariance and Gaussian Processes	E8, LN8, CH14	Richard
Nov 14	Measurement error and missingness	CH15	Richard
Nov 21	Generalized Linear Madness	CH16	Richard
Nov 28	Causality in research	-	Fredrik
Dec 5	Causality in research	-	Clemens
Dec 12	Multiverse analysis	-	Robert
Dec 19	Causal discovery	-	Moritz

* E = Optional exercises, LN = Lecture Notes, CH = Chapter in course book.

Objectives

Knowledge and understanding

• Describe and explain the concepts of probability space (incl. conditional probability), random variable, expected value and random processes, and know a number of concrete examples of the concepts.

• Describe Markov chain Monte Carlo methods such as Metropolis.

• Describe and explain Hamiltonian Monte Carlo.

• Explain and describe multicollinearity, post-treatment bias, collider bias, and confounding.

• Describe and explain ways to avoid overfitting.

Examination
Examination will be done through a case that you submit in written form. The case is an analysis that you either want to redo (from a previous study) or that you want to do for the first time (in a coming study). Our hope is that the written material can be part of a replication package, or the actual paper, when you later publish it.

Course literature
McElreath, R. (2020), 2nd edition, Statistical Rethinking: A Bayesian Course with Examples in R and Stan, CRC, Boca Raton, Florida. ISBN: 9780367139919
(We believe the book can be downloaded from Chalmers library.)
Accompanying videos for the book on YouTube https://www.youtube.com/playlist?list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus

Support for students with additional needs
In case you need any special support do not hesitate to contact the course responsible when the course starts or before the course.

Contacts
Course responsible and examiner: Richard Torkar torkarr@chalmers.se
Teachers:
Richard Torkar
Julian Frattini
Fredrik Johansson (guest lecture, CSE)
Clemens Wittenbecher (guest lecture, LIFE)
Robert Felt (guest lecture, CSE)
Moritz Schauer (guest lecture, MV)