Doubly Robust Estimation

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

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Latest papers 18

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OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments
Oct 7, 2026Tomàs Garriga, Valentyn Melnychuk, Konstantin Hess +4Causal Effect Estimation Causal Inference
Novartis · Barcelona Supercomputing Center · Universitat Polit`ecnica de Catalunya +2
Efficient Best-of-N policy evaluation for inference-time alignment
Oct 7, 2026Jonas Schweisthal, Yuxin Wang, Athiya Deviyani +2Best-of-N Sampling Off-Policy Evaluation
LMU Munich & MCML · Carnegie Mellon University · Meta (work in personal capacity)
Variance-Optimal Off-Policy Evaluation with Conjunct Effect Modeling
Oct 6, 2026Nicolò Felicioni, Michael Benigni, Maurizio Ferrari Dacrema +1Causal Effect Estimation Off-Policy Evaluation
Spotify · Politecnico di Milano
Recommendation Ranking Off-Policy Evaluation under Ranking-Dependent Examination via Examination-Relevance Decomposition
Sep 28, 2026Riki Okamura, Toshiharu SugawaraOff-Policy Evaluation Doubly Robust Estimation
Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan
Weighted Spline-Expanded Networks with Distributional Balancing for Continuous Treatment Effects
Sep 27, 2026Shucheng Liu, Chan Park, Guanhua ChenCausal Effect Estimation Doubly Robust Estimation
University of North Carolina at Chapel Hill · University of Illinois Urbana-Champaign · University of Wisconsin–Madison
Doubly Robust Estimation of Causal Effect on CVR with Targeted Regularization
Aug 13, 2026Jiayi Dan, Bo Li, Lu Deng +1Causal Effect Estimation Doubly Robust Estimation
Tsinghua University, Beijing, China · Tencent Inc., Shenzhen, China.
Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories
Jul 30, 2026Mengfei Ran, Yifeng Shen, Ruijie GuanCausal Representation Learning Causal Inference
Academy of Life and Natural Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, China · Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Beyond the Training Distribution: Evaluating Predictions Under Distribution Shift and Selection Bias
Jun 12, 2026Annie Ulichney, Amanda CostonDouble ML Distribution Shift Robustness
Department of Statistics, University of California, Berkeley
Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects
May 25, 2026Michael Fuchs, Dominik KreissCausal Effect Estimation Conditional Average Treatment Effect Estimation
Actuarial Department Allianz Versicherungs-AG Munich, Germany
Valid Best-Model Identification for LLM Evaluation via Low-Rank Factorization
May 11, 2026Elad Tolochinsky, Yaniv Tenzer, Yaniv RomanoModel Selection Multi-Armed Bandits
Department of Computer Science, Technion – Israel Institute of Technology · Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology
Doubly Robust Proxy Causal Learning with Neural Mean Embeddings
May 10, 2026Bariscan Bozkurt, Alexandre Galashov, Dimitri Meunier +3Causal Effect Estimation Causal Inference
University College London
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
Augmenting Human Evaluation with LLM Judges: How Many Human Reviews Do You Need?
May 8, 2026Jane Paik KimLLM Evaluation LLM-as-a-Judge
Department of Psychiatry and Behavioral Sciences Stanford University Stanford, CA 94304
Temporal Smoothness Doubly Robust Learning for Debiased Knowledge Tracing
May 7, 2026Peilin Zhan, Wei Chen, Weilin Chen +2Knowledge Tracing Doubly Robust Estimation
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
Copula-Based Endogeneity Correction for Doubly Robust Estimation of Treatment Effect
May 5, 2026Sahil Shikalgar, Md. Noor-E-AlamCausal Effect Estimation Causal Inference
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA
CASP: Support-Aware Offline Policy Selection for Two-Stage Recommender Systems
Apr 24, 2026Nilson ChapagainOff-Policy Evaluation Recommender Systems
Texas A&M University College Station, TX, USA
Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing
Mar 17, 2026Saksham Jain, Alex LuedtkeCausal Effect Estimation Causal Inference
Department of Statistics, University of Washington, Seattle, USA · Departments of Health Care Policy and Statistics, Harvard University, USA
Decomposing Discrimination: Causal Mediation Analysis for AI-Driven Credit Decisions
Date pendingDuraimurugan RajamanickamFinancial Services Causal Effect Estimation
VP, Artificial Intelligence, Hudson Valley Credit Union, Poughkeepsie, NY · PhD Candidate, Causal Machine Learning, University of Arkansas at Little Rock

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments

Tomàs Garriga, Valentyn Melnychuk, Konstantin Hess, Eduard Serrahima de Cambra+3

Novartis · Barcelona Supercomputing Center · Universitat Polit`ecnica de Catalunya+2

Causal Effect Estimation Causal Inference Doubly Robust Estimation Conditional Generative Modeling

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Estimating conditional distributional potential outcomes (CDPOs) over time is important in medicine (e.g., to estimate patient-specific risks under different treatment sequences). However, this task is challenging because of time-varying confounding, yet existing adjustment strategies for this task are limited. In this paper, we aim to learn CDPOs under time-varying treatments using flexible generative models. Our contributions are two-fold. (1) We introduce a tailored adjustment strategy for our setting, namely, generative recursive g-computation. Our adjustment strategy recursively propagates full conditional outcome distributions rather than conditional means, modeling the variables of interest directly rather than full trajectories. Building on our adjustment strategy, we formulate simple generative learners for CDPO estimation. However, these learners can be sensitive to nuisance estimation errors, which motivates an orthogonal learner. (2) We thus introduce OrthoGen, a Neyman-orthogonal and doubly robust generative learner. Importantly, we show that OrthoGen further achieves rate double robustness and quasi-oracle efficiency under suitable conditions. Our learners are flexible and can be instantiated with different generative backbones (e.g., normalizing flows and diffusion models). Across experiments with synthetic, semi-synthetic and real-world datasets, we find that OrthoGen is highly effective. To the best of our knowledge, we are the first to propose a generative orthogonal learner for estimating CDPOs under time-varying treatments.

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