1 citations · 1 across the 9 of their papers we have counts for
10 papers
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
Christophe Muller, Ayub Kharel, Alex Luedtke +7
Standard causal identification methods often assume no unmeasured confounding and can fail when relevant confounders are unobserved. Proximal causal inference instead uses proxy va…
Causal methods for LLM development and evaluation
Dennis Frauen, Marie Brockschmidt, Konstantin Hess +10
Large language model (LLM) development is currently driven by large-scale empirical iteration over data mixtures, reward models, routing strategies, and evaluation pipelines. Here,…
From Residuals to Reasons: LLM-Guided Mechanism Inference from Tabular Data
Mohammad R. Rezaei, Rahul G. Krishnan
A persistent challenge in machine learning for scientific applications is jointly achieving prediction and understanding. Statistical models excel on structured data but operate as…
Modular Multimodal Classification Without Fine-Tuning: A Simple Compositional Approach
Herman Bergström, Aditya Mehrotra, Rahul G. Krishnan
We introduce CoMET, \textit{\textbf{C}omposing \textbf{M}odality \textbf{E}ncoders with \textbf{T}abular foundation models}, a simple yet highly competitive method for multimodal c…
IV-ICL: Bounding Causal Effects with Instrumental Variables via In-Context Learning
Vahid Balazadeh, Hamidreza Kamkari, Medha Barath +2
The instrumental-variables (IV) setting is standard for partial identification of causal effects when unobserved confounding makes point identification impossible. Existing approac…
Causal Foundation Models with Continuous Treatments
Christopher Stith, Medha Barath, Vahid Balazadeh +2
Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines. Of particular importance across a variety of domains is the continuo…
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
Mohammad R. Rezaei, Tejas Balaji, Rahul G. Krishnan
Irregularly sampled multivariate event streams remain a difficult modality for generative modeling: tokenization-based approaches break down when inter-event intervals vary by orde…
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
Vahid Balazadeh, Hamidreza Kamkari, Valentin Thomas +4
Causal effect estimation from observational data is fundamental across various applications. However, selecting an appropriate estimator from dozens of specialized methods demands…
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…
A Geometric Explanation of the Likelihood OOD Detection Paradox
Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell +3
Likelihood-based deep generative models (DGMs) commonly exhibit a puzzling behaviour: when trained on a relatively complex dataset, they assign higher likelihood values to out-of-d…