9 citations · 25 across the 15 of their papers we have counts for
19 papers
Dynamic Policy Evaluation and Learning with Spatio-temporal Data
Lingxiao Zhou, Kosuke Imai, Jason Lyall +1
Although sequential decision-making is ubiquitous across domains, policy evaluation and learning with spatio-temporal data remain challenging due to spatial spillover and temporal…
Identification and efficient estimation of compliance and network causal effects in cluster-randomized trials
Chao Cheng, Georgia Papadogeorgou, Fan Li
Treatment noncompliance is pervasive in infectious disease cluster-randomized trials. Although all individuals within a cluster are assigned the same treatment condition, the treat…
Spatial vertical regression for spatial panel data: Evaluating the effect of the Florentine tramway's first line on commercial vitality
Giulio Grossi, Alessandra Mattei, Georgia Papadogeorgou
Synthetic control methods are commonly used in panel data settings to evaluate the effect of an intervention. In many of these cases, the treated and control units correspond to sp…
Spatiotemporal causal inference with arbitrary spillover and carryover effects: Airstrikes and insurgent violence in the Iraq War
Mitsuru Mukaigawara, Kosuke Imai, Jason Lyall +1
Social scientists now routinely draw on high-frequency, high-granularity ''microlevel'' data to estimate the causal effects of subnational interventions. To date, most researchers…
Estimating Heterogeneous Treatment Effects for Spatio-Temporal Causal Inference
Lingxiao Zhou, Kosuke Imai, Jason Lyall +1
Scholars from diverse fields increasingly rely on high-frequency spatio-temporal data. Yet, causal inference with these data remains challenging due to spatial spillover and tempor…
Addressing selection bias in cluster randomized experiments via weighting
Georgia Papadogeorgou, Bo Liu, Fan Li
In cluster randomized experiments, individuals are often recruited after the cluster treatment assignment, and data are typically only available for the recruited sample. Post-rand…
Spatial causal inference in the presence of unmeasured confounding and interference
Georgia Papadogeorgou, Srijata Samanta
This manuscript unites causal inference and spatial statistics, presenting novel insights for causal inference in spatial data analysis, and drawing from tools in spatial statistic…
Bayesian inference for aggregated Hawkes processes
Lingxiao Zhou, Georgia Papadogeorgou
The Hawkes process, a self-exciting point process, has a wide range of applications in modeling earthquakes, social networks and stock markets. The established estimation process r…
Discussion of the manuscript: Spatial+ a novel approach to spatial confounding
Georgia Papadogeorgou
I congratulate Dupont, Wood and Augustin (DWA hereon) for providing an easy-to-implement method for estimation in the presence of spatial confounding, and for addressing some of th…
Clarifying Selection Bias in Cluster Randomized Trials: Estimands and Estimation
Fan Li, Zizhong Tian, Jennifer Bobb +1
In cluster randomized trials, patients are typically recruited after clusters are randomized, and the recruiters and patients may not be blinded to the assignment. This often leads…
Covariate-informed latent interaction models: Addressing geographic & taxonomic bias in predicting bird-plant interactions
Georgia Papadogeorgou, Carolina Bello, Otso Ovaskainen +1
Reductions in natural habitats urge that we better understand species' interconnection and how biological communities respond to environmental changes. However, ecological studies…
Propensity Score Weighting for Causal Subgroup Analysis
Siyun Yang, Elizabeth Lorenzi, Georgia Papadogeorgou +3
A common goal in comparative effectiveness research is to estimate treatment effects on pre-specified subpopulations of patients. Though widely used in medical research, causal inf…
Causal Inference with Spatio-temporal Data: Estimating the Effects of Airstrikes on Insurgent Violence in Iraq
Georgia Papadogeorgou, Kosuke Imai, Jason Lyall +1
Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are gener…
Soft Tensor Regression
Georgia Papadogeorgou, Zhengwu Zhang, David B. Dunson
Statistical methods relating tensor predictors to scalar outcomes in a regression model generally vectorize the tensor predictor and estimate the coefficients of its entries employ…
Mitigating Unobserved Spatial Confounding when Estimating the Effect of Supermarket Access on Cardiovascular Disease Deaths
Patrick Schnell, Georgia Papadogeorgou
Confounding by unmeasured spatial variables has received some attention in the spatial statistics and causal inference literatures, but concepts and approaches have remained largel…
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…
Bipartite Causal Inference with Interference
Corwin M. Zigler, Georgia Papadogeorgou
Statistical methods to evaluate the effectiveness of interventions are increasingly challenged by the inherent interconnectedness of units. Specifically, a recent flurry of methods…
A causal exposure response function with local adjustment for confounding: Estimating health effects of exposure to low levels of ambient fine particulate matter
Georgia Papadogeorgou, Francesca Dominici
The Clean Air Act mandates that the National Ambient Air Quality Standards (NAAQS) must be routinely assessed to protect populations based on the latest science. Therefore, researc…
Adjusting for Unmeasured Spatial Confounding with Distance Adjusted Propensity Score Matching
Georgia Papadogeorgou, Christine Choirat, Corwin Zigler
Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confoundi…