[ABE-L] Seminários PIPGEs UFSCar/USP

[Michel H. Montoril]

Caros,

Gostaríamos de convidar a todos para o próximo seminário conjunto
UFSCar/ICMC-USP, que ocorrerá no dia *27/05*, *às 14h*. Seguem informações
abaixo.

Sintam-se à vontade para divulgar (arquivo em anexo) entre eventuais
interessados.

Por fim, também gostaria de manifestar os meus sinceros sentimentos aos
amigos e à família do Prof. Julio. Que ele descanse em paz, e que nós
possamos nos recuperar dessa enorme perda.

Saudações,
Michel

*Scheduled for:*
May 27, 2022, at 2:00 pm
(GMT-03:00) Brasilia Standard Time - Sao Paulo

*Video call link HERE <https://meet.google.com/kor-zzkw-ayi>.*

*Speaker:*
Adèle H. Ribeiro (Columbia University)

*Title:*
Causal Effect Identification in Partially Understood Domains

*Abstract:*
One pervasive task found throughout the empirical sciences is to determine
the effect of interventions from observational (non-experimental) data. It
is well-understood that assumptions are necessary to perform causal
inferences, which are commonly articulated through causal diagrams (Pearl,
2000). Despite the power of this approach, there are settings where the
knowledge necessary to fully specify a causal diagram may not be available,
particularly in complex, high-dimensional domains. In this talk, I will
present two novel causal effect identification approaches that relax the
stringent requirement of fully specifying a causal diagram. The first is a
new graphical modeling tool called cluster DAGs (for short, C-DAGs) that
allows for the specification of relationships among clusters of variables,
while the relationships between the variables within a cluster are left
unspecified. The second includes a complete calculus and algorithm for
effect identification from a Partial Ancestral Graph (PAG), which
represents a Markov equivalence class of causal diagrams, learnable from
observational data. These approaches are expected to help researchers and
data scientists to identify novel effects in real-world domains, where
knowledge is largely unavailable and coarse.

*Bio:*
Dr. Adèle Helena Ribeiro is a highlighted DAAD Postdoc-NeT-AI Fellowship
recipient and Postdoctoral Research Scientist in the Causal Artificial
Intelligence (Causal AI) Laboratory. Her research lies at the intersection
of Computer Science, Statistics, and Artificial Intelligence in Healthcare.
Her efforts are focused on advancing the theory of causal inference and
learning for discovering, generalizing, and personalizing cause-effect
relationships from multiple observational and experimental data
collections. She received her Ph.D., M.S., and B.S. degrees all from the
Institute of Mathematics and Statistics of the University of Sao Paulo
(USP), Brazil. Academic webpage: https://adele.github.io/.

===============================
/* Michel H. Montoril,
 * Assistant Professor,
 * Department of Statistics,
 * Federal University of São Carlos,
 * São Carlos, SP, 13565-905, Brazil
 */
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