Zobrazeno 1 - 10
of 315
pro vyhledávání: '"MEALLI, FABRIZIA"'
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
In many causal studies, outcomes are censored by death, in the sense that they are neither observed nor defined for units who die. In such studies, the focus is usually on the stratum of always survivors up to a single fixed time s. Building on a rec
Externí odkaz:
http://arxiv.org/abs/2401.00196
Autor:
Ballerini, Veronica, Bornkamp, Björn, Mattei, Alessandra, Mealli, Fabrizia, Wang, Craig, Zhang, Yufen
In clinical trials, patients may discontinue treatments prematurely, breaking the initial randomization and, thus, challenging inference. Stakeholders in drug development are generally interested in going beyond the Intention-To-Treat (ITT) analysis,
Externí odkaz:
http://arxiv.org/abs/2310.06653
In causal inference studies, interest often lies in understanding the mechanisms through which a treatment affects an outcome. One approach is principal stratification (PS), which introduces well-defined causal effects in the presence of confounded p
Externí odkaz:
http://arxiv.org/abs/2309.14486
Autor:
Forastiere, Laura, Mattei, Alessandra, Pescarini, Julia M., Barreto, Mauricio L., Mealli, Fabrizia
The Brazil Bolsa Familia (BF) program is a conditional cash transfer program aimed to reduce short-term poverty by direct cash transfers and to fight long-term poverty by increasing human capital among poor Brazilian people. Eligibility for Bolsa Fam
Externí odkaz:
http://arxiv.org/abs/2211.09099
This paper provides a critical review of the Bayesian perspective of causal inference based on the potential outcomes framework. We review the causal estimands, identification assumptions, the general structure of Bayesian inference of causal effects
Externí odkaz:
http://arxiv.org/abs/2206.15460
Publikováno v:
In Multiple Sclerosis and Related Disorders November 2024 91
In December 2017, two leading derivative exchanges, CBOE and CME, introduced the first regulated Bitcoin futures. Our aim is estimating their causal impact on Bitcoin volatility and trading volume. Employing a new causal approach, C-ARIMA, we find th
Externí odkaz:
http://arxiv.org/abs/2109.15052
In randomized experiments, interactions between units might generate a treatment diffusion process. This is common when the treatment of interest is an actual object or product that can be shared among peers (e.g., flyers, booklets, videos). For inst
Externí odkaz:
http://arxiv.org/abs/2109.07502
The Rubin Causal Model (RCM) is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes. In recent years, several methods have been developed under the RCM to estimate causal effects in time series s
Externí odkaz:
http://arxiv.org/abs/2103.06740