Applied Bayesian Statistics School 2023


Bayesian Causal Inference

Florence Center for Data Science, University of Florence
Firenze, Italy
12-16 June 2023

ABS Schools

The Applied Bayesian Statistics summer school has been running since 2004. It is organised by

  • IMATI CNR Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Milano

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 school is
Bayesian Causal Inference.

Course Outline and Programme

The aim of this course is to introduce the fundamental concepts and the state-of-the-art methods for causal inference under the potential outcomes framework, with an emphasis on the Bayesian inferential paradigm.
Topics will cover randomized experiments, common methods for observational studies, such as propensity score, matching, weighting and doubly-robust estimation, heterogeneous treatment effects, sensitivity analysis, instrumental variables, principal stratification, panel data methods, and longitudinal treatments. Recent advances related to high dimensional analysis and machine learning will be naturally incorporated into the discussion. All methods will be illustrated via real world case studies.

Tentative Program

  • Day 1: Fundamentals of the potential outcomes framework. Randomized experiments and methods for observational studies, including propensity scores, regression adjustment, matching, weighting, doubly-robust. Case studies on debit card use and household spending.
  • Day 2: General structure of Bayesian causal inference, model specification (linear, BART, GP, Bayesian casual forests, and other popular machine learning methods), heterogeneous treatment effects, implications in high dimensions, choice of prior.
  • Day 3: Role of propensity scores in Bayesian causal inference and sensitivity analysis.
  • Day 4: From instrumental variables to principal stratification, and implementation via STAN. Multiple case studies, including randomized experiment with noncompliance, and a regression discontinuity design.
  • Day 5: Methods for panel data: difference-in-differences and synthetic controls. Methods on longitudinal treatment: g-computation and marginal structural models, and their Bayesian versions. Multiple case studies in political science and medicine. Concluding remarks.

Details on 2023 Course

The University of Firenze can issue a certificate stating that the course is worth 3 ECTS credits.
Interested students can write to abs23@mi.imati.cnr.it


Practical sessions will make use of R and STAN, which should be installed in participants' computers.


(will be updated periodically before the summer school)
1. Ding P, Li F. 2018. Causal inference: a missing data perspective. Statistical Science. 33(2), 214-237.
2. Li F, Ding P, Mealli F. 2022. Bayesian causal inference: a critical review. Philosophical Transactions of the Royal Society A. Forthcoming. arXiv:2206.15460.
3. Linero AR, Antonelli JL. 2022. The how and why of Bayesian nonparametric causal inference. Wiley Interdisciplinary Reviews: Computational Statistics, e1583.

Location and Schedule

The 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, 12th June 2023, at 2 p.m.
end time - Friday, 16th June 2023, 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