Applied Bayesian Statistics School 2020


Bayesian Causal Inference

Florence Center for Data Science, University of Florence
Firenze, Italy
8-12 June 2020

Due to the virus outbreak, ABS20 has been postponed to
7-11 June 2021
in the same location in Firenze.
New registrations can be held starting from 1st November 2020.

ABS Schools

The Applied Bayesian Statistics summer school has been running since 2004. Since 2012 it is organised by

Local contributing organization:
Florence Center for Data Science (FDS)
Department of Statistics, Computer Science, Applications (DISIA)
University of Florence


The school aims to present state-of-the-art Bayesian applications, inviting leading experts in their field.
Each year a different topic is chosen.

Past editions were devoted to: Gene Expression Genomics, Decision Modelling in Health Care, Spatial Data in Environmental and Health Sciences, Bayesian Methods and Econometrics, Bayesian Decision Problems in Biostatistics and Clinical Trials, Bayesian Methodology for Clustering, Classification and Categorical Data Analysis, Bayesian Machine Learning with Biomedical Applications, Hierarchical Modeling for Environmental Processes, Stochastic Modelling for Systems Biology, Bayesian Methods for Variable Selection with Applications to High-Dimensional Data and Applied Bayesian Nonparametrics, Modern Bayesian Methods and Computing for the Social Sciences, Bayes Big Data and the Internet, Modeling Spatial And Spatio-Temporal Data With Environmental Applications, Bayesian Statistical Modelling and Analysis in Sport, Bayesian Demography.

Lecturer and Topic

The lecturer is Prof. Fan Li (Department of Statistical Science, Duke University, Durham, NC, USA).

The topic chosen for the 2021 school is
Bayesian Causal Inference.

Course Outline and Programme

The aim of this course is to introduce the fundamental concepts and state-of-art methods for causal inference under the potential outcome framework. The lectures will be organized by the treatment assignment mechanisms. Topics will cover randomized experiments, observational studies with ignorable assignment mechanisms, natural experiments, sequential ignorable longitudinal treatments.
Recent advances related to machine learning and more complex situations such as spatial-temporal treatments and interference will also be discussed. All methods will be illustrated via real case studies in health studies, economics and biology. Though the causal framework and most of the methods are independent of the inferential paradigm, an emphasis will be put on the Bayesian paradigm for inference.

Tentative Program

  • Day 1: Lectures on the fundamentals of the potential outcome framework. Introduction to randomized experiments and methods for observational studies, including propensity score methods, regression adjustment, matching, weighting, double-robust estimation. Also introduction to the inferential frameworks (Bayesian, frequentist and Fisherian). Discussion of Case Study 1 on debit card and spending.
  • Day 2: Lectures on natural experiments, including instrumental variables, regression discontinuity designs, difference-in-differences, synthetic controls. Discussion of Case Study 2 on financial aid and dropout in Italian colleges, and Case Study 3 on European Central Bank Corporate Sector Purchasing Program and bond performance.
  • Day 3: Lectures on principal stratification and mediation. Discussion of Case Study 4 on a randomized experiment with noncompliance and Case Study 5 on the mediation analysis between early adversity, social connectedness and health.
  • Day 4: Lectures on longitudinal treatments, heterogeneous treatment effects, and machine learning methods to causal inference. Discussion of Case Study 6 on online advertisement.
  • Day 5: Lectures on advanced topics including spatial-temporal treatments and interference. Concluding remarks.

Details on 2021 Course


Practical sessions will make use of R software which should be installed in participants' computers


(will be updated periodically before the summer school)
1. Imbens and Rubin. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press. (This is an easy-to-read but long book covering most of the material on Day 1, but not on the later lectures).
2. Ding, P, and Li, F. (2018). Causal inference: a missing data perspective. Statistical Science. 33(2), 214-237.

Location and Schedule

The 2021 school will be held at DiSIA - Department of Statistics, Computer Science, Applications; Viale Morgagni, 59 - Firenze.
Please note that the number of available places is limited.

School timetable:
start time - Monday, 7th June 2021, at 2 p.m.
end time - Friday, 11th June 2021, at 1 p.m..

Intended Audience & Past Editions

PhD or Masters students, post-docs, researchers not only in Statistics but also in Biostatistics, Epidemiology, Economics, Social Sciences and Policy.
Check list of participants or have a look at previous school editions.
Participant list Past Editions

Registration & Accommodation

Since a limited number of places is available, we strongly encourage participants to register as soon as possible. Please note that the registration form can be filled only if you are able to provide some data which are necessary according to the current Italian laws.
Registration Accommodation

Contact Us

If you have any question, please contact the ABS School Secretariat.
Contact Us