51 citations · 145 across the 32 of their papers we have counts for
33 papers
Difference-in-Differences in the Presence of Unknown Interference
Fabrizia Mealli, Javier Viviens
The stable unit treatment value (SUTVA) is a crucial assumption in the Difference-in-Differences (DiD) research design. It rules out hidden versions of treatment and any sort of in…
Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis
Andrea Ichino, Fabrizia Mealli, Javier Viviens
Every year, over one million EU students choose a secondary school track based on teacher recommendations, yet little evidence shows this yields optimal assignments. Using Dutch da…
Causal Inference when Intervention Units and Outcome Units Differ
Georgia Papadogeorgou, Zhaoyan Song, Guido Imbens +1
We study causal inference in settings characterized by interference with a bipartite structure. There are two distinct sets of units: intervention units to which an intervention ca…
Bayesian Nonparametrics for Principal Stratification with Continuous Post-Treatment Variables
Dafne Zorzetto, Antonio Canale, Fabrizia Mealli +2
Principal stratification provides a causal inference framework for investigating treatment effects in the presence of a post-treatment variable. Principal strata play a key role in…
Bayesian principal stratification with longitudinal data and truncation by death
Giulio Grossi, Marco Mariani, Alessandra Mattei +1
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 stra…
Evaluating causal effects on time-to-event outcomes in an RCT in Oncology with treatment discontinuation
Veronica Ballerini, Björn Bornkamp, Alessandra Mattei +3
In clinical trials, patients may discontinue treatments prematurely, breaking the initial randomization and, thus, challenging inference. Stakeholders in drug development are gener…
Principal stratification with continuous treatments and continuous post-treatment variables
Joseph Antonelli, Minxuan Wu, Fabrizia Mealli +2
Principal stratification (PS) is a commonly used approach for understanding the mechanisms through which a treatment affects an outcome. The goal of this work is to extend the PS f…
Selecting Subpopulations for Causal Inference in Regression Discontinuity Designs
Laura Forastiere, Alessandra Mattei, Julia M. Pescarini +2
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 increasin…
Bayesian Causal Inference: A Critical Review
Fan Li, Peng Ding, Fabrizia Mealli
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 assu…
Causal effect of regulated Bitcoin futures on volatility and volume
Fiammetta Menchetti, Fabrizio Cipollini, Fabrizia Mealli
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 an…
Causal Effects with Hidden Treatment Diffusion on Observed or Partially Observed Networks
Costanza Tortú, Irene Crimaldi, Fabrizia Mealli +1
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 tha…
Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach
Fiammetta Menchetti, Fabrizio Cipollini, Fabrizia Mealli
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 be…
From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework
Francesca Dominici, Falco J. Bargagli-Stoffi, Fabrizia Mealli
This paper discusses the fundamental principles of causal inference - the area of statistics that estimates the effect of specific occurrences, treatments, interventions, and expos…
Bipartite Interference and Air Pollution Transport: Estimating Health Effects of Power Plant Interventions
Corwin Zigler, Vera Liu, Fabrizia Mealli +1
Evaluating air quality interventions is confronted with the challenge of interference since interventions at a particular pollution source likely impact air quality and health at d…
Exploiting network information to disentangle spillover effects in a field experiment on teens' museum attendance
Silvia Noirjean, Marco Mariani, Alessandra Mattei +1
A key element in the education of youths is their sensitization to historical and artistic heritage. We analyze a field experiment conducted in Florence (Italy) to assess how appro…
Modelling Network Interference with Multi-valued Treatments: the Causal Effect of Immigration Policy on Crime Rates
C. Tortù, I. Crimaldi, F. Mealli +1
Policy evaluation studies, which intend to assess the effect of an intervention, face some statistical challenges: in real-world settings treatments are not randomly assigned and t…
Assessing causal effects in the presence of treatment switching through principal stratification
Alessandra Mattei, Peng Ding, Veronica Ballerini +1
Clinical trials often allow patients in the control arm to switch to the treatment arm if their physical conditions are worse than certain tolerance levels. For instance, treatment…
Causal inference and machine learning approaches for evaluation of the health impacts of large-scale air quality regulations
Rachel C. Nethery, Fabrizia Mealli, Jason D. Sacks +1
We develop a causal inference approach to estimate the number of adverse health events prevented by large-scale air quality regulations via changes in exposure to multiple pollutan…
Survivor average causal effects for continuous time: a principal stratification approach to causal inference with semicompeting risks
Leah Comment, Fabrizia Mealli, Sebastien Haneuse +1
In semicompeting risks problems, nonterminal time-to-event outcomes such as time to hospital readmission are subject to truncation by death. These settings are often modeled with i…
Matching on Generalized Propensity Scores with Continuous Exposures
Xiao Wu, Fabrizia Mealli, Marianthi-Anna Kioumourtzoglou +2
In the context of a binary treatment, matching is a well-established approach in causal inference. However, in the context of a continuous treatment or exposure, matching is still…
The Local Randomization Framework for Regression Discontinuity Designs: A Review and Some Extensions
Zach Branson, Fabrizia Mealli
Regression discontinuity designs (RDDs) are a common quasi-experiment in economics and statistics. The most popular methodologies for analyzing RDDs utilize continuity-based assump…
Evaluating Federal Policies Using Bayesian Time Series Models: Estimating the Causal Impact of the Hospital Readmissions Reduction Program
Georgia Papadogeorgou, Fiammetta Menchetti, Christine Choirat +3
Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and…
Estimating Causal Effects Under Interference Using Bayesian Generalized Propensity Scores
Laura Forastiere, Fabrizia Mealli, Albert Wu +1
In most real-world systems units are interconnected and can be represented as networks consisting of nodes and edges. For instance, in social systems individuals can have social ti…
Estimating Population Average Causal Effects in the Presence of Non-Overlap: The Effect of Natural Gas Compressor Station Exposure on Cancer Mortality
Rachel C. Nethery, Fabrizia Mealli, Francesca Dominici
Most causal inference studies rely on the assumption of overlap to estimate population or sample average causal effects. When data exhibit non-overlap, estimation of these estimand…