51 citations · 145 across the 32 of their papers we have counts for
19 papers · 1 filter
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…
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 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…
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…
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…
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…
Identification and estimation of treatment and interference effects in observational studies on networks
Laura Forastiere, Edoardo M. Airoldi, Fabrizia Mealli
Causal inference on a population of units connected through a network often presents technical challenges, including how to account for interference. In the presence of local inter…
Bayesian Inference for Sequential Treatments under Latent Sequential Ignorability
Federico Ricciardi, Alessandra Mattei, Fabrizia Mealli
We focus on causal inference for longitudinal treatments, where units are assigned to treatments at multiple time points, aiming to assess the effect of different treatment sequenc…
Principal Score Methods: Assumptions and Extensions
Avi Feller, Fabrizia Mealli, Luke Miratrix
Researchers addressing post-treatment complications in randomized trials often turn to principal stratification to define relevant assumptions and quantities of interest. One appro…
Posterior Predictive P-values with Fisher Randomization Tests in Noncompliance Settings: Test Statistics vs Discrepancy Variables
Laura Forastiere, Fabrizia Mealli, Luke Miratrix
In randomized experiments with noncompliance, tests may focus on compliers rather than on the overall sample. Rubin (1998) put forth such a method, and argued that testing for the…
Improving Inference of Gaussian Mixtures Using Auxiliary Variables
Andrea Mercatanti, Fan Li, Fabrizia Mealli
Expanding a lower-dimensional problem to a higher-dimensional space and then projecting back is often beneficial. This article rigorously investigates this perspective in the conte…