4 papers
Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors
Nikita Dhawan, Daniel Shen, Leonardo Cotta +1
Causal inference, especially in observational studies, relies on untestable assumptions about the true data-generating process. Sensitivity analysis helps us determine how robust o…
Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab
Haonan Duan, Stephen Zhewen Lu, Caitlin Fiona Harrigan +5
Designing experiments and result interpretations are core scientific competencies, particularly in biology, where researchers perturb complex systems to uncover the underlying syst…
End-To-End Causal Effect Estimation from Unstructured Natural Language Data
Nikita Dhawan, Leonardo Cotta, Karen Ullrich +2
Knowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, reg…
Test-Time Fairness and Robustness in Large Language Models
Leonardo Cotta, Chris J. Maddison
Frontier Large Language Models (LLMs) can be socially discriminatory or sensitive to spurious features of their inputs. Because only well-resourced corporations can train frontier…