Details

Monday June 12

13.00
Registration
14.00 - 16.00
Lecture: Introduction to the potential outcomes framework; Fisher's and Neyman's perspectives
16.00 - 16.30
Coffee break
16.30 - 18.00
Lecture: The Frequentists' world (part 1) on randomized experiments: covariate adjustment, stratified RCT, imputation
18.00 - 19.00
Practical session: analysis of randomized experiments

Tuesday June 13

9.00 - 10.30
Lecture: The Frequentists' world (part 2) on observational studies: propensity score, matching, weighting, outcome modeling, double-robust estimation
10.30 - 11.00
Coffee break
11.00 - 12.30
Practical session 1: common frequentists methods, e.g. matching and weighting, PSweight (including weighting and DR) and matching packages
12.30 - 14.00
Lunch
14.00 - 15.00
Lecture: Basic structure of Bayesian causal inference
15.00 - 15.15
Comfort break
15.15 - 16.30
Practical session 2: Basics of Bayesian causal inference, including RCT imputation, different versions of estimands and complex estimands
16.30 - 17.00
Coffee break
17.00 - 19.00
Participants' posters
Poster session Download details

How Drug Availability Affects Consumer Compliance: The Role of Market Concentration Jiancai Liao, Chuhan Liu, Jian Ni, Haizhong Wang

SmaC: Spatial Matrix Completion Method Giulio Grossi

Quantifying the impact of Covid-19 on the Italian labour market Antonio Pietrafesa

Non-Linear interaction models: an approach based on varying coefficients Davide Fabbrico, Matteo Pedone, Francesco Claudio Stingo

Bayesian Nonparametrics for Principal Stratification: an Application on Environmental Policies Effects on Health Dafne Zorzetto, Falco J. Bargagli-Stoffi, Antonio Canale, Fabrizia Mealli, Francesca Dominici

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks Vittorio Del Tatto, Gianfranco Fortunato, Domenica Bueti, Alessandro Laio

Bayesian estimation of P3b amplitude as a correlate of an internal Signal Detection Theory mechanism Erik Skjoldan Mortensen

Median-based Splitting Rules For The Causal Tree Karolina Gliszczynska, Lennard Maßmann

BART as a Gaussian Process Giacomo Petrillo

Estimating Treatment Effects Using BART Models Within The Conformal Inference Framework Lennard Maßmann

An investigation of the causal association between social participation and health outcomes: findings from a nationally representative sample using marginal structural models Shunqi Zhang, Nan Zhang, Mark Brown

Wednesday June 14

9.00 - 9.30
Lecture: Role of PS
9.30 - 10.30
Practical session 1: Basics of Bayesian casual inference (Veronica Ballerini)
10.30 - 11.00
Coffee break
11.00- 12.00
Lecture: Heterogeneous treatment effects/machine learning
12.00 - 12.15
Comfort break
12.15- 13.15
Practical session 2: practice of methods for heterogeneous treatment effects (Giacomo Petrillo)
13.15
Free afternoon and evening

Thursday June 15

9.00 - 10.30
Lecture: Sensitivity analysis
10.30 - 11.00
Coffee break
11.00 - 12.30
Lecture: Instrumental variables
12.30 - 14.00
Lunch
14.00 - 15.30
Lecture: Principal Stratification
15.30 - 16.00
Coffee break
16.00 - 18.00
Practical session: inference of principal stratification and PStrata
19.30
Farewell dinner

Friday June 16

9.00 - 10.30
Lecture: Causal inference with Bayesian time series models (Fiammetta Menchetti)
10.30 - 11.00
Coffee break
11.00 - 12.30
Practical session: Causal inference with Bayesian time series models
12.30 - 13.00
Wrap up
IMPORTANT NOTE:
It is important to have your own PC for the practical lessons. Remember to take it with you before leaving. Please install the following software on your PC in advance to start your lessons smoothly:
  • RStudio, RStan and R packages: PSweight, PStrata (that will automatically install RStan), Matching
REFERENCES:
  • Ding P, Li F. 2018. Causal inference: a missing data perspective. Statistical Science. 33(2), 214-237.
  • Li F, Ding P, Mealli F. 2022. Bayesian causal inference: a critical review. Philosophical Transactions of the Royal Society A. 381 (2247). 2022.0153 arXiv:2206.15460.
  • Linero AR, Antonelli JL. 2022. The how and why of Bayesian nonparametric causal inference. Wiley Interdisciplinary Reviews: Computational Statistics, e1583.