3 papers
cs.LG2026
Causal Risk Minimization for High-Dimensional Treatments
Nikita Dhawan, Arnav Paruthi, Andrew Kim +3
Predicting the effect of interventions with many possible variations, e.g., therapeutic content that affects mental health outcomes or an earnings call transcript that drives movem…
cs.LG2026
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…
cs.LG2024
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…