Conditional Counterfactual Mean Embeddings: Doubly Robust Estimation and Learning Rates

• URL: http://arxiv.org/abs/2602.04736v1
• Date: Wed, 04 Feb 2026 16:40:29 GMT
• Title: Conditional Counterfactual Mean Embeddings: Doubly Robust Estimation and Learning Rates
• Authors: Thatchanon Anancharoenkij, Donlapark Ponnoprat,
• Abstract summary: We propose a framework that embeds conditional distributions of counterfactual outcomes into a kernel reproducing Hilbert space (RKHS)<n>Under this framework, we develop a two-stage meta-estimator for CCME that accommodates any RKHS-valued regression in each stage.<n>Our experiments demonstrate that our estimators accurately recover distributional features including multimodal structure of conditional counterfactual distributions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A complete understanding of heterogeneous treatment effects involves characterizing the full conditional distribution of potential outcomes. To this end, we propose the Conditional Counterfactual Mean Embeddings (CCME), a framework that embeds conditional distributions of counterfactual outcomes into a reproducing kernel Hilbert space (RKHS). Under this framework, we develop a two-stage meta-estimator for CCME that accommodates any RKHS-valued regression in each stage. Based on this meta-estimator, we develop three practical CCME estimators: (1) Ridge Regression estimator, (2) Deep Feature estimator that parameterizes the feature map by a neural network, and (3) Neural-Kernel estimator that performs RKHS-valued regression, with the coefficients parameterized by a neural network. We provide finite-sample convergence rates for all estimators, establishing that they possess the double robustness property. Our experiments demonstrate that our estimators accurately recover distributional features including multimodal structure of conditional counterfactual distributions.

Related papers

• C-kNN-LSH: A Nearest-Neighbor Algorithm for Sequential Counterfactual Inference [18.510457926165373]
  Estimating causal effects from longitudinal trajectories is central to understanding the progression of complex conditions.<n>We introduce emphC-kNN--LSH, a nearest-neighbor framework for sequential causal inference.
  arXiv  Detail & Related papers  (2026-02-02T17:35:57Z)
• Direct Doubly Robust Estimation of Conditional Quantile Contrasts [8.035129168712334]
  Conditional quantile comparator (CQC) offers quantile-level summaries akin to the conditional quantile treatment effect (CQTE)<n>Current estimation is limited by the need to first estimate the difference in conditional cumulative distribution functions and then invert it.<n>We propose the first direct estimator of the CQC, allowing for explicit modelling and parameterisation.
  arXiv  Detail & Related papers  (2026-01-27T14:46:25Z)
• Distributional Evaluation of Generative Models via Relative Density Ratio [12.663086000741872]
  We propose a functional evaluation metric for generative models based on the relative density ratio (RDR)<n>We show that the RDR as a functional summary of the goodness-of-fit for the generative model, possesses several desirable theoretical properties.<n>We show that the estimated RDR not only allows for an effective comparison of the overall performance of competing generative models, but it can also offer a convenient means of revealing the nature of the underlying goodness-of-fit.
  arXiv  Detail & Related papers  (2025-10-29T13:31:35Z)
• Km-scale dynamical downscaling through conformalized latent diffusion models [45.94979929172337]
  Dynamical downscaling is crucial for deriving high-resolution meteorological fields from coarse-scale simulations.<n>Generative Diffusion models (DMs) have recently emerged as powerful data-driven tools for this task.<n>However, DMs lack finite-sample guarantees against overconfident predictions, resulting in miscalibrated grid-point-level uncertainty estimates.<n>We tackle this issue by augmenting the downscaling pipeline with a conformal prediction framework.
  arXiv  Detail & Related papers  (2025-10-15T08:41:36Z)
• Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings [23.3862001690226]
  Estimating the distribution of outcomes under counterfactual policies is critical for decision-making in domains such as recommendation, advertising, and healthcare.<n>We propose and analyze a novel framework-Counterfactual Policy Mean Embedding (CPME)-that represents the entire counterfactual outcome distribution in a reproducing kernel space (RKHS)<n>We introduce both a plug-in estimator and a doubly robust estimator; the latter enjoys improved convergence rates by correcting for bias in both the outcome embedding and propensity models.
  arXiv  Detail & Related papers  (2025-06-03T12:16:46Z)
• NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification [42.418429168532406]
  We introduce NeuralSurv, the first deep survival model to incorporate Bayesian uncertainty quantification.<n>For efficient posterior inference, we introduce a mean-field variational algorithm with coordinate-ascent updates that scale linearly in model size.<n>In experiments, NeuralSurv delivers superior calibration compared to state-of-the-art deep survival models.
  arXiv  Detail & Related papers  (2025-05-16T09:53:21Z)
• KANITE: Kolmogorov-Arnold Networks for ITE estimation [3.087385668501741]
  We introduce KANITE, a framework leveraging Kolmogorov-Arnold Networks (KANs) for Individual Treatment Effect (ITE) estimation under multiple treatments setting in causal inference.
  arXiv  Detail & Related papers  (2025-03-18T05:16:36Z)
• Kolmogorov-Smirnov GAN [52.36633001046723]
  We propose a novel deep generative model, the Kolmogorov-Smirnov Generative Adversarial Network (KSGAN) Unlike existing approaches, KSGAN formulates the learning process as a minimization of the Kolmogorov-Smirnov (KS) distance.
  arXiv  Detail & Related papers  (2024-06-28T14:30:14Z)
• Enabling Uncertainty Estimation in Iterative Neural Networks [49.56171792062104]
  We develop an approach to uncertainty estimation that provides state-of-the-art estimates at a much lower computational cost than techniques like Ensembles.<n>We demonstrate its practical value by embedding it in two application domains: road detection in aerial images and the estimation of aerodynamic properties of 2D and 3D shapes.
  arXiv  Detail & Related papers  (2024-03-25T13:06:31Z)
• Improving Neural Additive Models with Bayesian Principles [54.29602161803093]
  Neural additive models (NAMs) enhance the transparency of deep neural networks by handling calibrated input features in separate additive sub-networks. We develop Laplace-approximated NAMs (LA-NAMs) which show improved empirical performance on datasets and challenging real-world medical tasks.
  arXiv  Detail & Related papers  (2023-05-26T13:19:15Z)
• Distributional Reinforcement Learning with Dual Expectile-Quantile Regression [51.87411935256015]
  quantile regression approach to distributional RL provides flexible and effective way of learning arbitrary return distributions.<n>We show that distributional estimation guarantees vanish, and we empirically observe that the estimated distribution rapidly collapses to its mean.<n>Motivated by the efficiency of $L$-based learning, we propose to jointly learn expectiles and quantiles of the return distribution in a way that allows efficient learning.
  arXiv  Detail & Related papers  (2023-05-26T12:30:05Z)
• PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI [47.24613772568027]
  Compressed Sensing MRI aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging. We propose a novel end-to-end Probabilistic Under-sampling and Explicable Reconstruction neTwork, dubbed PUERT, to jointly optimize the sampling pattern and the reconstruction network. Experiments on two widely used MRI datasets demonstrate that our proposed PUERT achieves state-of-the-art results in terms of both quantitative metrics and visual quality.
  arXiv  Detail & Related papers  (2022-04-24T04:23:57Z)
• Distribution Regression with Sliced Wasserstein Kernels [45.916342378789174]
  We propose the first OT-based estimator for distribution regression. We study the theoretical properties of a kernel ridge regression estimator based on such representation.
  arXiv  Detail & Related papers  (2022-02-08T15:21:56Z)
• Exact Recovery in the General Hypergraph Stochastic Block Model [92.28929858529679]
  This paper investigates fundamental limits of exact recovery in the general d-uniform hypergraph block model (d-HSBM) We show that there exists a sharp threshold such that exact recovery is achievable above the threshold and impossible below it.
  arXiv  Detail & Related papers  (2021-05-11T03:39:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.