Overview

Causal inference has spawned renewed interest as a formal framework for answering scientific questions across many domains, spanning epidemiology, economics, medicine, artificial intelligence and more.

Understanding and predicting the effect of interventions is a fundamental component of effective policy and decision-making. The ultimate goal of a majority of scientific studies is to establish if and how changing certain conditions, policies, practices and behaviors will affect given outcomes of interest.

Questions about the consequences of an action are causal in nature. They are, therefore, best tackled within a causal inference framework, aiming to go beyond predictions to make causal predictions. Where appropriate, explicitly framing study questions with causal language has several advantages over common analytical approaches: it adds to the transparency of the objectives and the underlying structural assumptions, and it helps with the interpretation and clarification of the potential limitations of any analysis.

By reasoning about causality with directed acyclic graphs (DAGs) and describing data-generating mechanisms with causal diagrams, we can further enhance/improve the communications between scientific investigators, analysts and other stakeholders. Especially in the context of real-world data analysis, causal inference offers a coherent framework to strengthen the quality of the conclusions we can draw from quantitative analyses of empirical evidence to inform and guide decisions which carry the potential to impact society.

The course proposes itself as a gentle introduction to causal thinking, aiming to provide the key concepts and principles of causal inference, touching upon some of the most common tools for causal inference.

Audience

This course is addressed to researchers new to causal reasoning who wish to familiarize themselves with the causal language and grasp the foundational concepts for delving into the causal inference literature.

Learning outcomes

At the end of the course, the participants will be able to:

Formulate research questions in the language of causal inference.
Define causal effects.
Describe data-generating mechanisms with causal diagrams.
Identify potential sources of bias with the help of graphical criteria to inform the study design and analysis.
Choose appropriate methods to estimate causal contrasts/effects from real data, and implement them using the R statistical software.

Prerequisites

Knowledge / competencies

The course is targeted to life scientists who are already familiar with the R programming language and who have basic knowledge on statistics, especially regression modelling.

Additional knowledge such as how to use tydiverse or knowledge of survival analysis are a plus but are not mandatory.

The competences and knowledge levels required correspond to those taught in courses such as: First Steps with R in Life Sciences and Introduction to statistics with R.

Technical

You are required to bring your own laptop with the following software installed

R and Rstudio

As well as the following R libraries:

ggplot2
tydiverse

Application

The registration fees for academics are 200 CHF and 1000 CHF for for-profit companies.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Applications close on 10/06/2025. Deadline for free-of-charge cancellation is set to 10/06/2025. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

Venue and Time

This course will take place at the University of Basel.

The course will start at 9:00 and end around 17:00. Precise information will be provided to the participants in due time.

Additional information

Coordination: Valeria Di Cola, SIB Training Group.

We will recommend 0.5 ECTS credits for this course (given a passed exam at the end of the course).

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

Please note that participation in SIB courses is subject to our general conditions.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact training@sib.swiss.

Causal Inference