“Special Guest Seminar Series” is a particular seminar series organized by the Florence Center for Data Science that invites researchers and distinguished academicians from external university institutions to hold a lecture about their current research or academic work. It takes place several times during the year. People are invited to attend and the registration (free of charge) is available one week before the event on the website (under the Events page).
DiSIA Xmas Lecture 2022
22th of December 2022, from 11:00 AM -13.30 pm:
The Department of Statistics, Computer Science, Applications “G. Parenti” together with the Florence Center for Data Science organized the 2023 Christmas Lecture:
Fabrizia Mealli –Professor of Statistics at the Department of Statistics, Informatics, Applications “G. Parenti” – DiSIA of the University of Florence hold a lecture on
“Causal inference: past, present, future“
Click here to download the slides of the lecture.
Kosuke Imai - "Special Guest Seminar Series"
7TH OF October 2022, FROM 10.30-11.30 PM:
Kosuke Imai – Harvard University will present a seminar on
“Statistical Inference for Heterogeneous Treatment Effects and Individualized Treatment Rules Discovered by Generic Machine Learning in Randomized Experiments“
Abstract: Researchers are increasingly turning to machine learning (ML) algorithms to estimate heterogeneous treatment effects (HET) and develop individualized treatment rules (ITR) using randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain HET or produce efficacious ITR under practical settings with many covariates and small sample size. In addition, the quantification of estimation uncertainty remains a challenge. We develop a general approach to statistical inference for estimating HET and evaluating ITR discovered by a generic ML algorithm. We utilize Neyman's repeated sampling framework, which is solely based on the randomization of treatment assignment and random sampling of units. Unlike some of the existing methods, the proposed methodology does not require modeling assumptions, asymptotic approximation, or resampling methods. We extend our analytical framework to a common setting, in which the same experimental data is used to both train ML algorithms and evaluate HET/ITR. In this case, our statistical inference incorporates the additional uncertainty due to random splits of data used for cross-fitting.
Here you can download the slides of the Seminar.
Data Science Xmas Lecture 2021
16th of December 2021, from 3-4.30 pm:
Marina Vannucci – Noah Harding Professor of Statistics, Rice University holds a lecture on
“Bayesian Models for Microbiome Data with Variable Selection“
Abstract: I will describe Bayesian models developed for understanding how the microbiome varies within a population of interest. I will focus on integrative analyses, where the goal is to combine microbiome data with other available information (e.g. dietary patterns) to identify significant associations between taxa and a set of predictors. For this, I will describe a general class of hierarchical Dirichlet-Multinomial (DM) regression models which use spike-and-slab priors for the selection of the significant associations. I will also describe a joint model that efficiently embeds DM regression models and compositional regression frameworks, in order to investigate how the microbiome may affect the relation between dietary factors and phenotypic responses, such as body mass index. I will discuss the advantages and limitations of the proposed methods with respect to current standard approaches used in the microbiome community and will present results on the analysis of real datasets. If time allows, I will also briefly present extensions of the model to mediation analysis.
Click here for all the information to participate