Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Bargagli Stoffi, Falco J."'
Autor:
Zorzetto, Dafne, Canale, Antonio, Mealli, Fabrizia, Dominici, Francesca, Bargagli-Stoffi, Falco J.
Principal stratification provides a causal inference framework that allows adjustment for confounded post-treatment variables when comparing treatments. Although the literature has focused mainly on binary post-treatment variables, there is a growing
Externí odkaz:
http://arxiv.org/abs/2405.17669
Autor:
Chen, Kevin L., Bargagli-Stoffi, Falco J., Kim, Raphael C., Henneman, Lucas R. F., Nethery, Rachel C.
Pollution from coal-fired power plants has been linked to substantial health and mortality burdens in the US. In recent decades, federal regulatory policies have spurred efforts to curb emissions through various actions, such as the installation of e
Externí odkaz:
http://arxiv.org/abs/2404.13442
In this contribution, we propose machine learning techniques to predict zombie firms. First, we derive the risk of failure by training and testing our algorithms on disclosed financial information and non-random missing values of 304,906 firms active
Externí odkaz:
http://arxiv.org/abs/2306.08165
Several epidemiological studies have provided evidence that long-term exposure to fine particulate matter (PM2.5) increases mortality risk. Furthermore, some population characteristics (e.g., age, race, and socioeconomic status) might play a crucial
Externí odkaz:
http://arxiv.org/abs/2302.11656
We propose a new strategy to identify the impact of class rank, exploiting a "visible" primary school rank from teachers' exam grades, and an "invisible" rank from unreported standardized test scores. Leveraging a unique panel dataset on Italian stud
Externí odkaz:
http://arxiv.org/abs/2302.10026
Autor:
Bargagli-Stoffi, Falco J1 (AUTHOR) fbargaglistoffi@hsph.harvard.edu, Incerti, Fabio2 (AUTHOR) fabio.incerti@imtlucca.it, Riccaboni, Massimo2 (AUTHOR) fabio.incerti@imtlucca.it, Rungi, Armando2 (AUTHOR) fabio.incerti@imtlucca.it
Publikováno v:
Industrial & Corporate Change. Oct2024, Vol. 33 Issue 5, p1063-1097. 35p.
Autor:
McFarlin, Ethan, Bargagli-Stoffi, Falco J., Castro, Edgar, Schwartz, Joel, Dominici, Francesca, Amini, Heresh
Publikováno v:
In Environmental Modelling and Software January 2024 172
Publikováno v:
Harvard Data Science Review (2021)
This paper discusses the fundamental principles of causal inference - the area of statistics that estimates the effect of specific occurrences, treatments, interventions, and exposures on a given outcome from experimental and observational data. We e
Externí odkaz:
http://arxiv.org/abs/2012.06865
In health and social sciences, it is critically important to identify subgroups of the study population where there is notable heterogeneity of treatment effects (HTE) with respect to the population average. Decision trees have been proposed and comm
Externí odkaz:
http://arxiv.org/abs/2009.09036
Publikováno v:
In: Data Science for Economics and Finance (2021) Springer
Thanks to the increasing availability of granular, yet high-dimensional, firm level data, machine learning (ML) algorithms have been successfully applied to address multiple research questions related to firm dynamics. Especially supervised learning
Externí odkaz:
http://arxiv.org/abs/2009.06413