Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates via Generalized Riesz Regression

• URL: http://arxiv.org/abs/2511.08303v1
• Date: Wed, 12 Nov 2025 01:51:58 GMT
• Title: Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates via Generalized Riesz Regression
• Authors: Masahiro Kato,
• Abstract summary: We develop efficiency bounds and efficient estimators whose variance aligns with the efficiency bound.<n>In the analysis, we introduce two different data-generating processes: the one-sample setting and the two-sample setting.
• Score: 6.44705221140412
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study investigates treatment effect estimation in the semi-supervised setting, where we can use not only the standard triple of covariates, treatment indicator, and outcome, but also unlabeled auxiliary covariates. For this problem, we develop efficiency bounds and efficient estimators whose asymptotic variance aligns with the efficiency bound. In the analysis, we introduce two different data-generating processes: the one-sample setting and the two-sample setting. The one-sample setting considers the case where we can observe treatment indicators and outcomes for a part of the dataset, which is also called the censoring setting. In contrast, the two-sample setting considers two independent datasets with labeled and unlabeled data, which is also called the case-control setting or the stratified setting. In both settings, we find that by incorporating auxiliary covariates, we can lower the efficiency bound and obtain an estimator with an asymptotic variance smaller than that without such auxiliary covariates.

Related papers

• A Two-Stage Interpretable Matching Framework for Causal Inference [0.6215404942415159]
  Matching in causal inference from observational data aims to construct treatment and control groups with similar distributions of covariables.<n>We introduce a novel Two-stage Interpretable Matching framework for transparent and interpretable covariable matching.<n>We use these high- quality matches to estimate the conditional average treatment effects (CATEs)<n>Our results demonstrate that TIM improves CATE estimates, increases multivariate overlap, and scales effectively to high-dimensional data.
  arXiv  Detail & Related papers  (2025-04-13T16:17:52Z)
• Semiparametric conformal prediction [79.6147286161434]
  We construct a conformal prediction set accounting for the joint correlation structure of the vector-valued non-conformity scores.<n>We flexibly estimate the joint cumulative distribution function (CDF) of the scores.<n>Our method yields desired coverage and competitive efficiency on a range of real-world regression problems.
  arXiv  Detail & Related papers  (2024-11-04T14:29:02Z)
• Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification [6.920336485308536]
  Pseudo-labeling-based semi-supervised approaches suffer from two problems in image classification. We develop a self adaptive threshold pseudo-labeling strategy, which thresholds for each class can be dynamically adjusted to increase the number of reliable samples. In order to effectively utilise unlabeled data with confidence below the thresholds, we propose an unreliable sample contrastive loss.
  arXiv  Detail & Related papers  (2024-07-04T03:04:56Z)
• CKD: Contrastive Knowledge Distillation from A Sample-wise Perspective [48.99488315273868]
  We propose a contrastive knowledge distillation framework that achieves sample-wise logit alignment while preserving semantic consistency.<n>Our approach transfers "dark knowledge" through teacher-student contrastive alignment at the sample level.<n>We conduct comprehensive experiments across three benchmark datasets, including the CIFAR-100, ImageNet-1K, and MS COCO datasets.
  arXiv  Detail & Related papers  (2024-04-22T11:52:40Z)
• Efficient semi-supervised inference for logistic regression under case-control studies [3.5485531932219243]
  We consider an inference problem in semi-supervised settings where the outcome in the labeled data is binary. Case-control sampling is an effective sampling scheme for alleviating imbalance structure in binary data. We find out that with the availability of the unlabeled data, the intercept parameter can be identified in semi-supervised learning setting.
  arXiv  Detail & Related papers  (2024-02-23T14:55:58Z)
• Continuous Treatment Effects with Surrogate Outcomes [12.548638259932915]
  We study the role of surrogates in estimating continuous treatment effects. We propose a doubly robust method to efficiently incorporate surrogates in the analysis.
  arXiv  Detail & Related papers  (2024-01-31T20:50:18Z)
• Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly Detection [81.07346419422605]
  Anomaly detection aims at identifying deviant samples from the normal data distribution. Contrastive learning has provided a successful way to sample representation that enables effective discrimination on anomalies. We propose a novel hierarchical semi-supervised contrastive learning framework, for contamination-resistant anomaly detection.
  arXiv  Detail & Related papers  (2022-07-24T18:49:26Z)
• A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings [0.0]
  We develop a family of SS estimators which are more robust and (2) more efficient than their supervised counterparts. We further establish root-n consistency and normality of our SS estimators whenever the propensity score in the model is correctly specified. Our estimators are shown to be semi-parametrically efficient as long as all the nuisance functions are correctly specified.
  arXiv  Detail & Related papers  (2022-01-03T04:12:44Z)
• Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap [140.98628848491146]
  We introduce deconfounding scores, which induce better overlap without biasing the target of estimation. We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data. In particular, we show that this technique could be an attractive alternative to standard regularizations.
  arXiv  Detail & Related papers  (2021-04-12T18:50:11Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference [73.23326654892963]
  We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs.
  arXiv  Detail & Related papers  (2020-03-02T15:21:20Z)

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.