{"id":3695,"date":"2021-11-16T15:35:30","date_gmt":"2021-11-16T14:35:30","guid":{"rendered":"https:\/\/datascience.unifi.it\/?p=3695"},"modified":"2025-11-27T17:13:02","modified_gmt":"2025-11-27T16:13:02","slug":"sma-special-issue-on-advanced-statistical-modeling-and-causal-inference-with-complex-data-for-better-decision-making","status":"publish","type":"post","link":"https:\/\/datascience.unifi.it\/index.php\/2021\/11\/16\/sma-special-issue-on-advanced-statistical-modeling-and-causal-inference-with-complex-data-for-better-decision-making\/","title":{"rendered":"SMA special Issue on \u201cAdvanced statistical modeling and causal inference with complex data for better decision making\u201d"},"content":{"rendered":"

We would like to kindly remind you to consider submitting papers for the Special Issue of Statistical Methods & Applications on \u201cAdvanced statistical modeling and causal inference with complex data for better decision making<\/em>\u201d.<\/p>\n

Decisions in many fields – including medicine, public health, epidemiology, social science, economics and finance – depend critically both on empirical evidence and the appropriate evaluation\u00a0of 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 requires 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\u00a0studies with challenging data structures. We invite original methodological and empirical papers involving studies with complex settings\u00a0and complex data structures including causal inference. Specifically, papers related to the following methodological settings, types of studies and application fields are particularly welcomed, as\u00a0long as they pertain to the general theme of the special issue.<\/p>\n

Methodological settings<\/strong>: Bayesian methods; Directional statistics; Estimation of causal effects with interference, intermediate variable, heterogeneous effects, and complex treatment assignment mechanisms;\u00a0Estimation of causal moderation effects; Graphical models; Machine learning methods and algorithms; Matching methods; Model selection; Missing data imputation; Nonparametric estimation of treatment effects; Resampling methods; Robust estimation of treatment effects; Statistical learning methods; Use of instrumental variables.<\/p>\n

Data and type of studies<\/strong>: Assessment of performances in education; Big and high-dimensional data (e.g., causal studies with high-dimensional confounders, exposures and\/or mediators);\u00a0Complex data structures (e.g. where units are organized in hierarchies or in social, geographical, physical, and economic networks);\u00a0Covid-19 data; Data with nonstandard domains (e.g. studies where data have an underlying structure that is a non-Euclidean space such as compositional or spherical data);\u00a0Evaluation of public policies or socio-economic programs; Irregular\/hybrid designs (e.g., causal studies with confounded post-treatment intermediate variables);\u00a0Study designs and\/or use of large and messy data sources.<\/p>\n

Application domains<\/strong>: Medicine, health, biology, epidemiology; Economics, policy and social sciences; Finance and econometrics.<\/p>\n

The deadline 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\u00a0https:\/\/www.springer.com\/journal\/10260\/updates\/19305000<\/a><\/p>\n

We look forward to receiving your submissions.<\/p>\n

Apologies for cross-posting.<\/p>\n

Guest Editors
\nClaudio Conversano (conversa@unica.it<\/a>), University of Cagliari, Italy,
\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":"

We would like to kindly remind you to consider submitting papers for the Special Issue of Statistical Methods & Applications on \u201cAdvanced statistical modeling and causal inference with complex data …<\/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":[79,78],"tags":[],"class_list":["post-3695","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-journals","category-research"],"_links":{"self":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/posts\/3695","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=3695"}],"version-history":[{"count":1,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/posts\/3695\/revisions"}],"predecessor-version":[{"id":3696,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/posts\/3695\/revisions\/3696"}],"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=3695"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/categories?post=3695"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tags?post=3695"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}