Semiparametric Inference

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Semiparametric inference studies how to estimate and quantify uncertainty for targets such as treatment effects or policy values when parts of the data-generating process are modeled flexibly. Common approaches use influence functions and tangent-space geometry to derive efficiency bounds, then apply orthogonal scores, debiased one-step estimators, or targeted maximum likelihood with cross-fitting. These methods are used with machine-learned nuisance functions in causal studies, survival analysis, missing-data problems, and adaptive experiments. A central challenge is maintaining valid confidence intervals under nuisance-estimation error, weak overlap, dependence, or adaptive sampling without imposing assumptions so strong that useful efficiency gains disappear.

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33 papers5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Semi-Linear Gaussian Bayesian Networks: Enhancing Flexibility in Node Dependencies via penalized B-Splines
Oct 22, 2026Aitor Oviedo-Madrid, Mabel Morales-Otero, Jose Gonzalez-Gomariz +1Bayesian Network Structure Learning Bayesian Networks
Institute of Data Science and Artiﬁcial Intelligence (DATAI), University of Navarra, Pamplona, Spain · TECNUN School of Engineering, University of Navarra, San Sebastián, Spain
Debiased Prior-Data Fitted Networks for Amortized Bayesian Semi-Parametric Inference
Oct 20, 2026Valentyn Melnychuk, Emil Javurek, Stefan FeuerriegelPrior-Data Fitted Networks Amortized Inference
LMU Munich & Munich Center for Machine Learning, Munich, Germany
Inference on the Value of Policy Improvement in Adaptive Experiments
Oct 11, 2026Justin Whitehouse, Emma Brunskill, Vasilis SyrgkanisPolicy Evaluation Semiparametric Inference
Management Science and Engineering, Stanford University · Computer Science, Stanford University
Data Fusion for Errors-in-Variables
Oct 5, 2026Huali Zhao, Molei Liu, Tianying WangData Fusion Parameter Estimation
School of Mathematics and Statistics, Huazhong University of Science and Technology · Department of Biostatistics, Peking University Health Science Center; Beijing International Center for Mathematical Research, Peking University · Department of Statistics, Colorado State University
Hybrid Neural Simulation-Based Inference for Robust Applications and Limited-Budget Scenarios
Sep 28, 2026Sean Benevedes, Mani Dehghan, Aishik Ghosh +1Simulation-Based Inference Cost-Aware Inference
School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA · School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA and Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA · Max Planck Institute for Physics, Boltzmannstr. 8, 85748 Garching, Germany
Sharp Structure-Agnostic Minimax Risk for Partial Linear Models
Sep 7, 2026Haichen Hu, David Simchi-LeviDouble ML Semiparametric Inference
Center for Computational Science and Engineering, MIT · Department of Civil and Environmental Engineering, MIT · Institute for Data, Systems, and Society, MIT
When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation
Aug 30, 2026Cong CaoSemiparametric Inference Causal Effect Estimation
Department of Biostatistics, Yale University, New Haven, CT 06520, USA
$\texttt{findr}$ : Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions
Aug 25, 2026Victor Medina-Olivares, Stefan Lessmann, Jonathan CrookInterpretable ML Credit Scoring
University of Edinburgh, Edinburgh, UK · Humboldt-Universität zu Berlin, Berlin, Germany
Debiased Inference for AI-Generated Data without Gold-Standard Labels: Identification via Multiple Imperfect Measurements
Aug 18, 2026Naoki Egami, Sooahn ShinSemiparametric Inference Latent Variable Models
Department of Political Science and Statistics and Data Science Center, Massachusetts Institute of Technology, Cambridge, MA 02139 · Department of Political Science, Massachusetts Institute of Technology, Cambridge, MA 02139
A Characterization of the Orthocomplement of the Tangent Space of Semiparametric Markov Models
Jul 26, 2026Trung Phung, Ilya ShpitserSemiparametric Inference Markov Models
Computer Science Department, Johns Hopkins University, Baltimore, Maryland, USA
Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity
Jul 12, 2026Roberto Faleh, Sofia Morelli, Holger BrandtSemiparametric Inference Parameter Estimation
Methods Center University of Tübingen, Germany
Orthogonal Discrepancy Kernels for Learning with Partial Physics
Jun 19, 2026Swapnil Manna, Timothy J. Rogers, Lawrence BullSemiparametric Inference Nonlinear System Identification
IISER Pune, India · University of Sheffield, UK · University of Glasgow, UK
Prediction-Powered Causal Inference by Automatic Debiased Machine Learning and Semi-Supervised Riesz Regression
Jun 11, 2026Masahiro KatoSemiparametric Inference Parameter Estimation
Data Analytics Department, Mizuho-DL Financial Technology, Co., Ltd.
Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
May 26, 2026Jakub Wornbard, Zikai Shen, Dimitri Meunier +1Semiparametric Inference Kernel Methods
University College London
Geometry Adaptive Counterfactual Distribution Learning with Diffusion-Guided Smoothing
May 25, 2026Kwangho KimSemiparametric Inference Causal Inference
Department of Statistics, Korea University
Parametric Rates for Nonparametric Conditional-Moment Bilevel Optimization
May 20, 2026Fares El Khoury, Houssam Zenati, Nathan Kallus +2Semiparametric Inference Hypergradient Optimization
Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK · Gatsby Computational Neuroscience Unit, University College London · Cornell University +1
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
May 8, 2026Houssam Zenati, Arthur GrettonSemiparametric Inference Causal Effect Estimation
1Gatsby Computational Neuroscience Unit, University College London
Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
May 3, 2026Hanxiao Chen, Debarghya MukherjeeSemiparametric Inference Multi-Task Learning
Department of Mathematics and Statistics, Boston University.
LLM Evaluation as Tensor Completion: Low Rank Structure and Semiparametric Efficiency
Apr 7, 2026Jiachun Li, David Simchi-Levi, Will Wei SunSemiparametric Inference LLM Evaluation
Massachusetts Institute of Technology · Purdue University
MEC: Machine-Learning-Assisted Generalized Entropy Calibration for Semi-Supervised Mean Estimation
Apr 7, 2026Se Yoon Lee, Jae Kwang KimPrediction-Powered Inference Semi-Supervised Learning
Texas A&M University · Iowa State University
Nearly Optimal Best Arm Identification for Semiparametric Bandits
Apr 5, 2026Seok-Jin KimBest-Arm Identification Multi-Armed Bandits
Columbia University
Learning Nonlinear Regime Transitions via Semi-Parametric State-Space Models
Apr 3, 2026Prakul Sunil HiremathRegime-Switching Dynamical Systems State Space Models
Visvesvaraya Technological University, Belagavi, India · Aliens on Earth (AoE) Autonomous Research Group, Belagavi, India
Beyond Consistency: Inference for the Relative risk functional in Deep Nonparametric Cox Models
Mar 25, 2026Sattwik Ghosal, Xuran Meng, Yi LiSurvival Analysis Semiparametric Inference
Department of Biostatistics, University of Michigan
Kernel Single-Index Bandits: Estimation, Inference, and Learning
Mar 19, 2026Sakshi Arya, Satarupa Bhattacharjee, Bharath K. SriperumbudurContextual Bandits Single-Index Models
Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University · Department of Statistics, University of Florida · Department of Statistics, The Pennsylvania State University
Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
Mar 12, 2026Valentyn Melnychuk, Vahid Balazadeh, Stefan Feuerriegel +1Causal Effect Estimation Prior-Data Fitted Networks
LMU Munich & Munich Center for Machine Learning (MCML), Munich, Germany · University of Toronto & Vector Institute, Toronto, Canada
Spatially Robust Inference with Predicted and Missing at Random Labels
Mar 11, 2026Stephen Salerno, Zhenke Wu, Tyler McCormickConfidence Region Estimation Learning with Missing Data
Fred Hutch Cancer Center · University of Michigan · University of Washington
Kernel Debiased Plug-in Estimation based on the Universal Least Favorable Submodel
Mar 9, 2026Haiyi Chen, Yang Liu, Ivana MalenicaSemiparametric Inference
Department of Biostatistics, UNC at Chapel Hill, USA · Department of Statistics & Operations Research, UNC at Chapel Hill, USA
Conditional Rank-Rank Regression via Deep Conditional Transformation Models
Mar 7, 2026Xiaoyi Wang, Long Feng, Zhaojun WangConditional Distribution Estimation Semiparametric Inference
Nankai University
Robust Weighted Triangulation of Causal Effects Under Model Uncertainty
Mar 1, 2026Rohit Bhattacharya, Ina Ocelli, Ted WestlingSemiparametric Inference Causal Effect Estimation
Dept. of Computer Science, Williams College · Dept. of Mathematics and Statistics, University of Massachusetts Amherst
Efficient Inference after Directionally Stable Adaptive Experiments
Feb 25, 2026Zikai Shen, Houssam Zenati, Nathan Kallus +3Adaptive Inference Semiparametric Inference
University College London · Gatsby Computational Neuroscience Unit, University College London · Cornell University +3

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Semi-Linear Gaussian Bayesian Networks: Enhancing Flexibility in Node Dependencies via penalized B-Splines

Aitor Oviedo-Madrid, Mabel Morales-Otero, Jose Gonzalez-Gomariz, Ruben Armañanzas

Institute of Data Science and Artiﬁcial Intelligence (DATAI), University of Navarra, Pamplona, Spain · TECNUN School of Engineering, University of Navarra, San Sebastián, Spain

Bayesian Network Structure Learning Bayesian Networks Semiparametric Inference

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Bayesian networks for continuous variables often rely on restrictive assumptions: linear Gaussian models lack flexibility to capture nonlinear dependencies, while non-parametric approaches provide greater flexibility at a high computational cost. This creates a gap for modeling scenarios where variables are approximately Gaussian but exhibit nonlinear relationships. We introduce Semi-Linear Gaussian Bayesian Networks (SLGBN), a novel class of continuous Bayesian networks designed to bridge this gap, preserving Gaussian conditional distributions while introducing nonlinear conditional mean functions. The proposed framework provides a unified probabilistic framework combining linear Gaussian and nonlinear Gaussian conditional models within the same Bayesian network Nonlinear conditional mean functions are estimated using generalized additive models (GAMs) with penalized B-splines. We present parameter estimation procedures for both types of nodes, as well as a greedy hill-climbing algorithm for learning the network structure under the SLGBN framework. To assess its performance, we compare SLGBN with other continuous Bayesian network models, including semiparametric Bayesian networks (SPBN) and Linear Gaussian Bayesian networks (LGBN). Experimental results evaluate both goodness of fit and the ability to recover the underlying network structure. The results show that, in the presence of nonlinear Gaussian relationships, SLGBN achieves better fit and improved structure recovery compared to alternative models, particularly when combined with the Extended Bayesian Information Criterion (EBIC*) score. Furthermore, by retaining Gaussian conditional distributions, SLGBN offers computational advantages over fully semiparametric approaches.