[ABE-L] Fwd: ISBA-BNP webinar: 14th June - Antonio Canale and Dafne Zorzetto

[Hedibert Lopes]

---------- Forwarded message ---------
From: ISBA-BNP Program Chair Jim Griffin <j.griffin em ucl.ac.uk>
Date: Wed, Jun 7, 2023 at 8:36 AM
Subject: ISBA-BNP webinar: 14th June - Antonio Canale and Dafne Zorzetto
To: Prof. Hedibert Lopes <hedibert em gmail.com>


Dear all,

We are delighted to announce a new webinar of the series organized by
BNP-ISBA, the Bayesian Nonparametric section of ISBA.

The zoom link of the webinar will be available the day before at
https://bnp-isba.github.io/webinars.html, where you can also find the list
of upcoming webinars.

DATE & TIME: 17:00 UTC on Wednesday June 14, 2023. Note that 17:00 UTC
corresponds to 1 pm US Eastern and 7 pm Central European.

SPEAKERS: Antonio Canale and Dafne Zorzetto (Universita degli studi di
Padova)
TITLE:  Dependent nonparametric priors for causal inference problems

Abstract: Bayesian nonparametric methods have gained significant traction
across various applied contexts, with a notable surge in attention directed
towards their applications in causal inference. In this talk, we present
two notable causal inference problems and demonstrate the valuable impact
of tailored dependent nonparametric priors. Both approaches employ
dependent nonparametric mixtures, which make a valuable contribution within
the classical Rubin's missing potential outcome framework. Firstly, we
address the problem of estimating the conditional average treatment effect
(CATE), introducing a confounder-dependent mixture model to capture causal
effect heterogeneity. Our method utilizes the flexibility of a dependent
Dirichlet process to model the distribution of potential outcomes
conditioned on confounders. This enables us to estimate individual
treatment effects, identify distinct population groups with similar CATEs,
and estimate causal effects within each identified group. Secondly, we
focus on principal stratification, a popular concept in health and
environmental sciences. However, when dealing with continuous
post-treatment variables, principal stratification poses several
inferential challenges. One such challenge involves identifying latent
principal strata using information on the heterogeneous response of the
intermediate variable to treatment. Here, we leverage a dependent mixture
prior with shared atoms to discover the principal strata and specifically
characterize the dissociative stratum, representing the stratum where no
effect of the exposure is observed. Both models are illustrated through
applications analyzing the impact of pollution levels on health.


Best regards

--
Jim Griffin
ISBA - BNP Section
Program Chair 2022-2023
e-mail: j.griffin em ucl.ac.uk
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-- 
Hedibert Freitas Lopes, PhD
Professor of Statistics and Econometrics
INSPER - Institute of Education and Research
Rua Quatá, 300 - São Paulo, SP 04546-042 Brazil
Phone: +55 11 4504-2343
www.hedibert.org
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