Course syllabus

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Welcome to the course FDAT002, Causal Inference and Bayesian Analysis. This Canvas course page contains all relevant material and provides a central forum for communication, both from us course instructors to you but also among yourselves.

The expectation is that this will be a useful course for you, and hopefully it will inspire you to further interest in this topic and, thus, also start using these approaches in your own research. The aim is that you will conduct a more advanced analysis of data you have collected in your own research.

Content

Overview

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.

Schedule

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.
Date Title Material* Teacher
2025-09-12 Introduction C1-3, LN1, E1 Richard Torkar
2025-09-19 Design of models I C4-6, LN2, E2 Julian Frattini
2025-09-26 Design of models II C4-6 Julian Frattini
2025-10-03 Information theory and maximum entropy C7, LN3, LN3.1, E3 Richard Torkar
2025-10-10 Interactions and sampling C8-9, LN4, E4 Richard Torkar
2025-10-17 Generalized linear models C10-11, LN5, E5 Richard Torkar
2025-10-24 Funky distributions C12, LN6, E6 Richard Torkar
2025-10-31 Multilevel models C13, LN7, E7 Richard Torkar
2025-11-07 Covariance and Gaussian processes C14, LN8, E8 Richard Torkar
2025-11-14 Measurement error and missingness C15 Richard Torkar
2025-11-21

Generalized linear madness
Presentation of individual cases

 C16 Everyone
2025-11-28 Causality in research Fredrik Johansson
2025-12-05 Causality in research Clemens Wittenbecher
2025-12-12 Multiverse analysis Robert Feldt
2025-12-19 Causal discovery Moritz Schauer
2026-01-29 Beyond Rethinking Slides Richard Torkar, Julian Frattini

* C = chapters in the course book, NL = lecture notes, E = (optional) exercises

Note that this schedule is preliminary and may be subject to change.

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.

Skills and abilities

  • Assess suitability of and apply methods of analysis on data .
  • Analyse descriptive statistics and decide on appropriate analysis methods.
  • Design statistical models mathematically and implement said models in a programming language (R is used in the course, but several other options exist).
  • Make use of random processes, e.g., Bernoulli, Binomial, Gaussian, and Poisson distributions, with over-dispersed outcomes.
  • Make use of ordered categorical outcomes (ordered-logit) and predictors.
  • Assess suitability of, from a ontological (natural process) and epsitemological (maximum entropy) perspective, various statistical distributions.
  • Make use of and assess directed acyclic graphs to argue causality.

Judgement and approaches

  • State and discuss the tools used for data analysis and, in particular, judge their output.
  • Assess diagnostics from Hamiltonian Monte Carlo and quadratic approximation using information theoretical concepts, e.g., information entropy, WAIC, and PSIS-LOO.
  • Judge posterior probability distributions for out of sample predictions and conduct posterior predictive checks.

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). The book is accompanied by

We strongly recommend sticking to the standard version of the book, first. 

Organizational

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

Teachers:

  • Richard Torkar (torkarr@chalmers.se), course responsible and examiner
  • Julian Frattini (julian.frattini@chalmers.se), teaching assistant
  • Fredrik Johansson (CSE), guest lecturer
  • Clemens Wittenbecher (LIFE), guest lecturer
  • Robert Felt (CSE), guest lecturer
  • Moritz Schauer (MV), guest lecturer

Course summary:

Course Summary
Date Details Due