[Forum SIS] Call for Papers - SMA Special Issue on “Advanced statistical modeling and causal inference with complex data for better decision making”

[datascience a unifi.it]

Dear all,

The Florence Center for Data Science is happy to promote the following 
opportunity.

The guest editors are glad to announce a Special Issue of Statistical 
Methods & Applications on “Advanced statistical modeling and causal 
inference with complex data for better decision making”.

Decisions in many fields — including medicine, public health, 
epidemiology, social science, economics and finance — depend critically 
both on empirical evidence and the appropriate evaluation of causal 
effects of competing treatments, exposures and/or policies. Nowadays 
data proliferates at an extraordinary pace, providing an endless source 
of information, but also raising new challenges that strain researchers’ 
ability to analyze and contextualize it. Drawing insights from large and 
complex data and from data having complex spaces as domain require new 
tools and the expertise and the research activities from different 
disciplines including statistics, computer science, and mathematics. 
Over the last years, there has been a growing number of studies, 
applying and extending statistical methods and causal inference methods 
to harness the power of data.

This special issue of Statistical Methods and Applications is dedicated 
to collect papers on cutting-edge methodological developments and unique 
applications to analyze studies and causal studies with challenging data 
structures. Contributions proposing advanced statistical methods and 
models and causal inference methods to deal with novel study designs, 
large and messy data sources, data with nonstandard domains, and complex 
treatment assignment mechanisms are welcome. From a methodological 
perspective, the special issue calls for papers developing and/or 
evaluating an innovative methodology for the analysis of studies with 
big or high-dimensional data — e.g., causal studies with 
high-dimensional confounders, exposures and/or mediators —, studies 
where data have an underlying structure that is a non-Euclidean space — 
e.g., analysis of compositional data or directional data, studies with 
irregular/hybrid designs — e.g., causal studies with confounded 
post-treatment intermediate variables, — and studies with complex data 
structures where units are organized in hierarchies or networks — e.g., 
social, geographical, physical, and economic networks — that give rise 
to interference issues due to the presence of ties among units, to 
different positions in the network, or to different underlying 
structures. Applications to biological, epidemiological and medical 
data, case studies related to the evaluation of public policies or 
socio-economic programs, and uses of causal inference methodologies for 
the assessment of performances in education are welcome. Nevertheless, 
there is no restriction on the subject matter: any interesting 
applications from any fields fall within the aim and scope of the 
special issue.

The deadline for manuscript submissions is January 15th, 2022.

Submissions should be made in the usual way, online at 
https://www.editorialmanager.com/smap/default.aspx, selecting ‘SI: 
Advanced statistical modeling and causal inference’ during the 
submission step ‘Additional Information.’


Full details are available at

https://www.springer.com/journal/10260/updates/19305000

We look forward to receiving your submissions.

Apologies for cross-posting.

The Guest Editors
Peng Ding (pengdingpku a berkeley.edu), University of California - 
Berkeley, USA
Alessandra Mattei (alessandra.mattei a unifi.it),  University of Florence, 
Italy
Agnese Panzera (agnese.panzera a unifi.it), University of Florence, Italy,
Giancarlo Ragozini (giancarlo.ragozini a unina.it), University of Naples 
Federico II, Italy