[Forum SIS] Seminario del DiSIA (Università di Firenze): Laura Forastiere

[Raffaele Guetto]

DiSIA  (Dipartimento di Statistica, Informatica, Applicazioni "G.
Parenti")
Università di Firenze

- PROSSIMO SEMINARIO - 

 21 Novembre 2018 ore 12.00

LAURA FORASTIERE (Yale Institute for Network Science - Yale University) 
terrà il seguente seminario:  

Estimating Causal Effects On Social Networks
In most real-world systems units are interconnected and can be
represented as networks consisting of nodes and edges. For instance, in
social systems individuals can have social ties, family or financial
relationships. In settings where some units are exposed to a treatment
and its effects spills over connected units, estimating both the direct
effect of the treatment and spillover effects presents several
challenges. First, assumptions on the way and the extent to which
spillover effects occur along the observed network are required. Second,
in observational studies, where the treatment assignment is not under
the control of the investigator, confounding and homophily are potential
threats to the identification and estimation of causal effects on
networks. Here, we make two structural assumptions: i) neighborhood
interference, which assumes interference to operate only through a
function of the the immediate neighbors' treatments, ii)
unconfoundedness of the individual and neighborhood treatment, which
rules out the presence of unmeasured confounding variables, including
those driving homophily. Under these assumptions we develop a new
covariate-adjustment estimator for treatment and spillover effects in
observational studies on networks. Estimation is based on a generalized
propensity score that balances individual and neighborhood covariates
across units under different levels of individual treatment and of
exposure to neighbors' treatment. Adjustment for propensity score is
performed using a penalized spline regression. Inference capitalizes on
a three-step Bayesian procedure which allows taking into account the
uncertainty in the propensity score estimation and avoiding model
feedback. Finally, correlation of interacting units is taken into
account using a community detection algorithm and incorporating random
effects in the outcome model. All these sources of variability,
including variability of treatment assignment, are accounted for in in
the posterior distribution of finite-sample causal estimands.This is a
joint work with Edo Airoldi, Albert Wu and Fabrizia Mealli.

Referente: Fabrizia Mealli

Il seminario sara' tenuto presso l'aula 32 del DiSIA, Viale Morgagni n.
59 - 50134 [1]  Firenze.

Tutti gli interessati sono cordialmente invitati a partecipare. 

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RAFFAELE GUETTO
_University of Florence - Department of Statistics, Informatics,
Applications_
Viale Morgagni, 59 - 50134 Firenze
Tel: +39 055 2751553
Web:
https://www.disia.unifi.it/p-doc2-2017-000000-G-3f2c342938302c-0.html 

Links:
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[1] tel:59%20-%2050134
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