1)
Vanessa Didelez
2)
Constantine Frangakis
3)
Gianluca Baio
4)
Fabio Cavallini
5)
Leonardo Grilli
6)
Anna Gottard
7)
Giovanni Marchetti
8)
Alessandra Mattei
9)
Fabrizia Mealli
10)
Carla Rampichini
11)
Federico Stefanini
12)
Eva Riccomagno
13)
Giovanni Pistone
14)
Guido Consonni
15)
Luca La Rocca
16) Bruno Scarpa
17)
Claudia Tarantola
18)
Piero Veronese
19)
Paola Vicard
Partecipanti
(non residenti)
20)
Antonio Forcina
21)
Francesco Bartolucci
22)
Elena Stanghellini
23)
Fulvia Pennoni
24)
Paolo Eusebi
25)
Daniela Cotana
26)
Sonia Lupparelli
27)
Simona Pacillo
28)
Bruno Bertaccini
1) Vanessa Didelez: “Tutorial on Graphical Models and
Causal Inference”
2) Constantine Frangakis: “Causal Inference Using Potential Outcomes:
Introduction and New Challenges”
2) Grilli Mealli: “Estimating Direct and Indirect Effect: a Discussion of
Some Approaches ”
4) Gianluca Baio e Davide Cavallini :
"Structural Learning in decision theoretic approach to causal
inference"
5) Eva Riccomagno : "Chain event graphs to
represent Baysian causal hypothesis"
6) Giovanni Pistone : “Equivalence of DAGs as
toric models”
2) Antonio Forcina: “Discussion
Cliccare qui per una selezione di foto (Thanks to
Giovanni Pistone) Foto 1 . Foto 2 .
Foto 3 . Foto 4 . Foto 5 . Foto 6 . Per chi h
rimasto a cena: cliccare
qui per una selezione di foto (Thanks to Bruno Scarpa).
J. Pearl (2003): "Statistics and Causal
Inference: a review"
D.R. Rubin (2005): "Causal Inference Using
Potential Outcomes: Design, Modelin, Decisions"
