Liberal Arts and Sciences
Papadogeorgou Lab
Led by Georgia Papadogeorgou · Statistics
What we study
The Papadogeorgou Lab studies causal inference and Bayesian modeling, focusing on developing methods to analyze data flexibly and solve real-world scientific questions. Their work includes research on compliance effects in trials and interference in policy evaluation.
What working here is like
- Computational
- No organisms (data only)
- Coding required
- Projects
- 3
- Papers
- 5
Current projects
Identification and efficient estimation of compliance and network causal effects in cluster-randomized trials
Developing methods to evaluate treatment effects in trials where individuals may not comply with assigned treatments.
Low-rank Covariate Balancing Estimators under Interference
Creating estimators to reduce variance in causal inference when outcomes affect each other.
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Causal Inference when Intervention and Outcome Units Differ
Studying causal effects when interventions and outcomes are measured on different groups of units.
Summaries are written by AI in plain language from public symposium abstracts and may be imperfect.
Recent papers, explained
- Spatial causal inference in the presence of unmeasured confounding and interference
2026 · The Annals of Applied Statistics
Combines causal inference and spatial statistics to analyze spatial data, showing how confounding and interference can distort results.
- Addressing selection bias in cluster randomized experiments via weighting
2025 · Biometrics
Uses weighting to correct selection bias in cluster experiments where participants are recruited after treatment assignment.
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- Bayesian inference for aggregated Hawkes processes
2025 · Bayesian Analysis
Develops a Bayesian method to estimate Hawkes process parameters from aggregated data, useful for modeling events like earthquakes.
- Evaluating federal policies using Bayesian time series models: estimating the causal impact of the hospital readmissions reduction program
2023 · Health Services and Outcomes Research Methodology
- Covariate-Informed Latent Interaction Models: Addressing Geographic & Taxonomic Bias in Predicting Bird–Plant Interactions
2023 · Journal of the American Statistical Association
Creates a model to predict bird-plant interactions, accounting for errors and biases in existing data.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Georgia Papadogeorgou hasn't set up this page yet. Their UF Scholars profile lists their publications.