791 citations · 2.5k across the 58 of their papers we have counts for
36 papers · 1 filter
FlowSDR: Sufficient Dimension Reduction via Conditional Normalizing Flows
Yuexiao Dong, Kenichiro Mcalinn, Edoardo Airoldi +1
Sufficient dimension reduction (SDR) seeks a low-dimensional linear projection of predictors that preserves the conditional distribution of the response. Existing methods target th…
Dynamic causal inference with time series data
Tanique Schaffe-Odeleye, Kōsaku Takanashi, Vishesh Karwa +2
We generalize the potential outcome framework to time series with an intervention by defining causal effects on stochastic processes. Interventions in dynamic systems alter not onl…
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…
Independent-Set Design of Experiments for Estimating Treatment and Spillover Effects under Network Interference
Chencheng Cai, Xu Zhang, Edoardo M. Airoldi
Interference is ubiquitous when conducting causal experiments over networks. Except for certain network structures, causal inference on the network in the presence of interference…
Bayesian Causal Synthesis for Meta-Inference on Heterogeneous Treatment Effects
Shonosuke Sugasawa, Kosaku Takanashi, Kenichiro McAlinn +1
The estimation of heterogeneous treatment effects in the potential outcome setting is biased when there exists model misspecification or unobserved confounding. As these biases are…
Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure
Christina Lee Yu, Edoardo M Airoldi, Christian Borgs +1
Randomized experiments are widely used to estimate the causal effects of a proposed treatment in many areas of science, from medicine and healthcare to the physical and biological…
Optimizing Randomized and Deterministic Saturation Designs under Interference
Chencheng Cai, Jean Pouget-Abadie, Edoardo M. Airoldi
Randomized saturation designs are a family of designs which assign a possibly different treatment proportion to each cluster of a population at random. As a result, they generalize…
Latent Space Network Modelling with Hyperbolic and Spherical Geometries
Marios Papamichalis, Kathryn Turnbull, Simon Lunagomez +1
A rich class of network models associate each node with a low-dimensional latent coordinate that controls the propensity for connections to form. Models of this type are well estab…
Causal inference with bipartite designs: A generalized propensity score approach
Nick Doudchenko, Minzhengxiong Zhang, Evgeni Drynkin +5
Bipartite experiments, in which one set of units receives a treatment while outcomes are measured on another set, have recently garnered attention for their ability to capture inte…
Latent Space Modelling of Hypergraph Data
Kathryn Turnbull, Simón Lunagómez, Christopher Nemeth +1
The increasing prevalence of relational data describing interactions among a target population has motivated a wide literature on statistical network analysis. In many applications…
Evaluating and Optimizing Network Sampling Designs: Decision Theory and Information Theory Perspectives
Simón Lunagómez, Marios Papamichalis, Patrick J. Wolfe +1
Some of the most used sampling mechanisms that implicitly leverage a social network depend on tuning parameters; for instance, Respondent-Driven Sampling (RDS) is specified by the…
A systematic investigation of classical causal inference strategies under mis-specification due to network interference
Vishesh Karwa, Edoardo M. Airoldi
We systematically investigate issues due to mis-specification that arise in estimating causal effects when (treatment) interference is informed by a network available pre-intervent…
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…
Optimizing cluster-based randomized experiments under a monotonicity assumption
Jean Pouget-Abadie, David C. Parkes, Vahab Mirrokni +1
Cluster-based randomized experiments are popular designs for mitigating the bias of standard estimators when interference is present and classical causal inference and experimental…
Limitations of design-based causal inference and A/B testing under arbitrary and network interference
Guillaume Basse, Edoardo Airoldi
Randomized experiments on a network often involve interference between connected units; i.e., a situation in which an individual's treatment can affect the response of another indi…
Testing for arbitrary interference on experimentation platforms
Jean Pouget-Abadie, Martin Saveski, Guillaume Saint-Jacques +4
Experimentation platforms are essential to modern large technology companies, as they are used to carry out many randomized experiments daily. The classic assumption of no interfer…
Elements of estimation theory for causal effects in the presence of network interference
Daniel L. Sussman, Edoardo M. Airoldi
Randomized experiments in which the treatment of a unit can affect the outcomes of other units are becoming increasingly common in healthcare, economics, and in the social and info…
SLANTS: Sequential Adaptive Nonlinear Modeling of Vector Time Series
Qiuyi Han, Jie Ding, Edoardo Airoldi +1
We propose a method for adaptive nonlinear sequential modeling of vector-time series data. Data is modeled as a nonlinear function of past values corrupted by noise, and the underl…
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…
Non-standard conditionally specified models for non-ignorable missing data
Alexander M Franks, Edoardo M Airoldi, Donald B Rubin
Data analyses typically rely upon assumptions about missingness mechanisms that lead to observed versus missing data. When the data are missing not at random, direct assumptions ab…
Observational studies with unknown time of treatment
Guillaume W. Basse, Alexander Volfovsky, Edoardo M. Airoldi
Time plays a fundamental role in causal analyses, where the goal is to quantify the effect of a specific treatment on future outcomes. In a randomized experiment, times of treatmen…
Model-assisted design of experiments in the presence of network correlated outcomes
Guillaume W. Basse, Edoardo M. Airoldi
We consider the problem of how to assign treatment in a randomized experiment, in which the correlation among the outcomes is informed by a network available pre-intervention. Work…
Towards stability and optimality in stochastic gradient descent
Panos Toulis, Dustin Tran, Edoardo M. Airoldi
Iterative procedures for parameter estimation based on stochastic gradient descent allow the estimation to scale to massive data sets. However, in both theory and practice, they su…
Causal inference for ordinal outcomes
Alexander Volfovsky, Edoardo M. Airoldi, Donald B. Rubin
Many outcomes of interest in the social and health sciences, as well as in modern applications in computational social science and experimentation on social media platforms, are or…