1 citations · 2 across the 27 of their papers we have counts for
33 papers
Perturbative methods for non-parametric instrumental variable
Wei Bu, Arthur Gretton
We introduce a perturbative approach for nonparametric instrumental variable (NPIV) estimation. By drawing inspiration from perturbation theory in physics, we extend standard kerne…
Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
Jakub Wornbard, Zikai Shen, Dimitri Meunier +1
We develop semiparametrically efficient inference for kernel measures of noise heterogeneity in additive noise models. In many applications, the regression function is estimated us…
The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
Victoria Lin, Taedong Yun, Maja Matarić +3
Large language models (LLMs) show potential as simulators of human behavior, offering a scalable way to study responses to interventions. However, because LLMs are trained largely…
Spectral Souping: A Unified Framework for Online Preference Alignment
Yinlam Chow, Guy Tennenholtz, Ted Yun +4
Reinforcement Learning from Human Feedback (RLHF) effectively aligns Large Language Models (LLMs) with aggregate human preferences but often fails to address the diverse and confli…
Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
Zikai Shen, Nathan Kallus, Dimitri Meunier +3
We consider debiased inference on finite-dimensional functionals of infinite-dimensional least-squares solutions to inverse problems as a way to avoid having to assume exact soluti…
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
Houssam Zenati, Arthur Gretton
Distributional treatment effects can be invisible to means: a treatment may preserve average outcomes while changing tails, modes, dispersion, or rare-event probabilities. Kernel t…
Efficient Inference after Directionally Stable Adaptive Experiments
Zikai Shen, Houssam Zenati, Nathan Kallus +3
We study inference on scalar-valued pathwise differentiable targets after adaptive data collection, such as a bandit algorithm. We introduce a novel target-specific condition, dire…
On the Hardness of Conditional Independence Testing In Practice
Zheng He, Roman Pogodin, Yazhe Li +3
Tests of conditional independence (CI) underpin a number of important problems in machine learning and statistics, from causal discovery to evaluation of predictor fairness and out…
Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression
Dimitri Meunier, Jakub Wornbard, Vladimir R. Kostic +5
We address the problem of causal effect estimation in the presence of hidden confounders using nonparametric instrumental variable (IV) regression. An established approach is to us…
Nonparametric Instrumental Variable Regression with Observed Covariates
Zikai Shen, Zonghao Chen, Dimitri Meunier +3
We study the problem of nonparametric instrumental variable regression with observed covariates, which we refer to as NPIV-O. Compared with standard nonparametric instrumental vari…
Towards a Unified Analysis of Neural Networks in Nonparametric Instrumental Variable Regression: Optimization and Generalization
Zonghao Chen, Atsushi Nitanda, Arthur Gretton +1
We establish the first global convergence result of neural networks for two stage least squares (2SLS) approach in nonparametric instrumental variable regression (NPIV). This is ac…
Kernel Treatment Effects with Adaptively Collected Data
Houssam Zenati, Bariscan Bozkurt, Arthur Gretton
Adaptive experiments improve efficiency by adjusting treatment assignments based on past outcomes, but this adaptivity breaks the i.i.d.\ assumptions that underpin classical asympt…
Demystifying Spectral Feature Learning for Instrumental Variable Regression
Dimitri Meunier, Antoine Moulin, Jakub Wornbard +2
We address the problem of causal effect estimation in the presence of hidden confounders, using nonparametric instrumental variable (IV) regression. A leading strategy employs spec…
Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
Houssam Zenati, Bariscan Bozkurt, Arthur Gretton
Estimating the distribution of outcomes under counterfactual policies is critical for decision-making in domains such as recommendation, advertising, and healthcare. We propose and…
Density Ratio-Free Doubly Robust Proxy Causal Learning
Bariscan Bozkurt, Houssam Zenati, Dimitri Meunier +2
We study the problem of causal function estimation in the Proxy Causal Learning (PCL) framework, where confounders are not observed but proxies for the confounders are available. T…
Regularized least squares learning with heavy-tailed noise is minimax optimal
Mattes Mollenhauer, Nicole Mücke, Dimitri Meunier +1
This paper examines the performance of ridge regression in reproducing kernel Hilbert spaces in the presence of noise that exhibits a finite number of higher moments. We establish…
Density Ratio-based Proxy Causal Learning Without Density Ratios
Bariscan Bozkurt, Ben Deaner, Dimitri Meunier +2
We address the setting of Proxy Causal Learning (PCL), which has the goal of estimating causal effects from observed data in the presence of hidden confounding. Proxy methods accom…
Learning-Order Autoregressive Models with Application to Molecular Graph Generation
Zhe Wang, Jiaxin Shi, Nicolas Heess +2
Autoregressive models (ARMs) have become the workhorse for sequence generation tasks, since many problems can be modeled as next-token prediction. While there appears to be a natur…
Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression
Juno Kim, Dimitri Meunier, Arthur Gretton +2
We provide a convergence analysis of deep feature instrumental variable (DFIV) regression (Xu et al., 2021), a nonparametric approach to IV regression using data-adaptive features…
Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation
Eleni Sgouritsa, Virginia Aglietti, Yee Whye Teh +3
The reasoning abilities of Large Language Models (LLMs) are attracting increasing attention. In this work, we focus on causal reasoning and address the task of establishing causal…
A Unified Data Representation Learning for Non-parametric Two-sample Testing
Xunye Tian, Liuhua Peng, Zhijian Zhou +3
Learning effective data representations has been crucial in non-parametric two-sample testing. Common approaches will first split data into training and test sets and then learn da…
Nonparametric Instrumental Regression via Kernel Methods is Minimax Optimal
Dimitri Meunier, Zhu Li, Tim Christensen +1
We study the kernel instrumental variable (KIV) algorithm, a kernel-based two-stage least-squares method for nonparametric instrumental variable regression. We provide a convergenc…
Interventional Processes for Causal Uncertainty Quantification
Hugh Dance, Peter Orbanz, Arthur Gretton
Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an entire function rather than a scalar es…
Foundations of Multivariate Distributional Reinforcement Learning
Harley Wiltzer, Jesse Farebrother, Arthur Gretton +1
In reinforcement learning (RL), the consideration of multivariate reward signals has led to fundamental advancements in multi-objective decision-making, transfer learning, and repr…