13 citations · 17 across the 15 of their papers we have counts for
14 papers · 1 filter
Higher-order Spillover Effects Under Partial Interference
Qixiang Xu, Laura Forastiere
Interference, under which a unit's outcome is affected by the treatment of other units through network connections, is often present when units interact on a network. When the netw…
Inward and Outward Spillover Effects of One Unit's Treatment on Network Neighbors under Partial Interference
Fei Fang, Edoardo M. Airoldi, Laura Forastiere
In settings where interference is present, direct effects are commonly defined as the average effect of a unit's treatment on their own outcome while fixing the treatment status or…
Identifying Key Influencers using an Egocentric Network-based Randomized Design
Zhibing He, Junhan Fan, Ashley Buchanan +2
Behavioral health interventions, such as trainings or incentives, are implemented in settings where individuals are interconnected, and the intervention assigned to some individual…
Estimation and inference for causal spillover effects in egocentric-network randomized trials in the presence of network membership misclassification
Ariel Chao, Donna Spiegelman, Ashley Buchanan +1
To leverage peer influence and increase population behavioral changes, behavioral interventions often rely on peer-based strategies. A common study design that assesses such strate…
Design of egocentric network-based studies to estimate causal effects under interference
Junhan Fang, Donna Spiegelman, Ashley Buchanan +1
Many public health interventions are conducted in settings where individuals are connected to one another and the intervention assigned to randomly selected individuals may spill o…
Evaluating Spillover Effects in Network-Based Studies In the Presence of Missing Outcomes
TingFang Lee, Ashley L. Buchanan, Natallia Katenka +3
Estimating causal effects in the presence of spillover among individuals embedded within a social network is often challenging with missing information. The spillover effect is the…
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…
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 Causal Effects of HIV Prevention Interventions with Interference in Network-based Studies among People Who Inject Drugs
TingFang Lee, Ashley L. Buchanan, Natallia V. Katenka +4
Evaluating causal effects in the presence of interference is challenging in network-based studies of hard-to-reach populations. Like many such populations, people who inject drugs…
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…
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…
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…
More Powerful Multiple Testing in Randomized Experiments with Non-Compliance
Joseph J. Lee, Laura Forastiere, Luke Miratrix +1
Two common concerns raised in analyses of randomized experiments are (i) appropriately handling issues of non-compliance, and (ii) appropriately adjusting for multiple tests (e.g.,…
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…