The Florence Center for Data Science is happy to promote the following opportunity.<\/p>\n
The guest editors are glad to announce a Special Issue of Statistical Methods & Applications on \u201cAdvanced statistical modeling and causal inference with complex data for better decision making\u201d<\/em><\/p>\n Decisions in many fields \u2014 including medicine, public health, epidemiology, social science, economics and finance \u2014 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\u2019 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.<\/p>\n 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 \u2014 e.g., causal studies with high-dimensional confounders, exposures and\/or mediators \u2014, studies where data have an underlying structure that is a non-Euclidean space \u2014 e.g., analysis of compositional data or directional data, studies with irregular\/hybrid designs \u2014 e.g., causal studies with confounded post-treatment intermediate variables, \u2014 and studies with complex data structures where units are organized in hierarchies or networks \u2014 e.g., social, geographical, physical, and economic networks \u2014 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.<\/p>\n The deadline<\/strong> for manuscript submissions is January 15th, 2022<\/strong>.<\/p>\n Submissions should be made in the usual way, online at https:\/\/www.editorialmanager.com\/smap\/default.aspx<\/a>, selecting \u2018SI: Advanced statistical modeling and causal inference\u2019 during the submission step \u2018Additional Information.\u2019<\/p>\n Full details are available at<\/p>\n https:\/\/www.springer.com\/journal\/10260\/updates\/19305000<\/a><\/p>\n We look forward to receiving your submissions.<\/p>\n The Guest Editors 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 \u201cAdvanced …<\/p>\n","protected":false},"author":1,"featured_media":2860,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[79,77,78],"tags":[],"class_list":["post-3499","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-journals","category-opportunities","category-research"],"_links":{"self":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/posts\/3499","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/comments?post=3499"}],"version-history":[{"count":3,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/posts\/3499\/revisions"}],"predecessor-version":[{"id":3502,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/posts\/3499\/revisions\/3502"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/media\/2860"}],"wp:attachment":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/media?parent=3499"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/categories?post=3499"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tags?post=3499"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nPeng Ding (pengdingpku@berkeley.edu<\/a>), University of California – Berkeley, USA
\nAlessandra Mattei (alessandra.mattei@unifi.it<\/a>), \u00a0University of Florence, Italy
\nAgnese Panzera (agnese.panzera@unifi.it<\/a>), University of Florence, Italy,
\nGiancarlo Ragozini (giancarlo.ragozini@unina.it<\/a>), University of Naples Federico II, Italy<\/p>\n","protected":false},"excerpt":{"rendered":"