Scuola/Corso (In evidenza)

   Giu 12, 2023

  • Scadenza: Giu 16, 2023
  • Chi: Prof. Fan Li (Department of Statistical Science, Duke University, Durham, NC, USA)
  • Dove: Florence Center for Data Science, University of Florence Firenze, Italy
  • Responsabile: Fabrizio Ruggeri


Applied Bayesian Statistics Summer School

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

The topic chosen for the 2023 school is: Bayesian Causal Inference
It is organized by IMATI CNR Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche

Course Outline

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.

Progetti Collegati