37 citations · 78 across the 38 of their papers we have counts for
10 papers · 1 filter
BACON: Budgeted Human Calibration for Modeling and Evaluation with Multiple AI Judges
Lei Shi, Anlan Zhang, Rita Lyu +6
AI judges offer a scalable, low-cost alternative to human evaluation, but their outputs can be biased relative to human preferences and highly item-dependent, varying across judges…
The Curse of Multiple Mediators: Hidden Interaction Effects in Activation Patching
Sankaran Vaidyanathan, David Arbour, Aaron Mueller +2
Activation patching is the primary tool in mechanistic interpretability. It attributes causal responsibility for a model behavior to each of its individual components by estimating…
Regularizing Extrapolation in Causal Inference
David Arbour, Harsh Parikh, Bijan Niknam +3
Many common estimators in machine learning and causal inference are linear smoothers, where the prediction is a weighted average of the training outcomes. Some estimators, such as…
Relational Causal Discovery with Latent Confounders
Matteo Negro, Andrea Piras, Ragib Ahsan +2
Estimating causal effects from real-world relational data can be challenging when the underlying causal model and potential confounders are unknown. While several causal discovery…
Sequential Causal Discovery with Noisy Language Model Priors
Prakhar Verma, David Arbour, Sunav Choudhary +3
Causal discovery from observational data typically assumes access to complete data and availability of perfect domain experts. In practice, data often arrive in batches, are subjec…
Leveraging Graph Diffusion Models for Network Refinement Tasks
Puja Trivedi, Ryan Rossi, David Arbour +7
Most real-world networks are noisy and incomplete samples from an unknown target distribution. Refining them by correcting corruptions or inferring unobserved regions typically imp…
Sample Constrained Treatment Effect Estimation
Raghavendra Addanki, David Arbour, Tung Mai +2
Treatment effect estimation is a fundamental problem in causal inference. We focus on designing efficient randomized controlled trials, to accurately estimate the effect of some tr…
Off-Policy Evaluation in Embedded Spaces
Jaron J. R. Lee, David Arbour, Georgios Theocharous
Off-policy evaluation methods are important in recommendation systems and search engines, where data collected under an existing logging policy is used to estimate the performance…
Inferring Individual Level Causal Models from Graph-based Relational Time Series
Ryan Rossi, Somdeb Sarkhel, Nesreen Ahmed
In this work, we formalize the problem of causal inference over graph-based relational time-series data where each node in the graph has one or more time-series associated to it. W…
Balanced off-policy evaluation in general action spaces
Arjun Sondhi, David Arbour, Drew Dimmery
Estimation of importance sampling weights for off-policy evaluation of contextual bandits often results in imbalance - a mismatch between the desired and the actual distribution of…