Doubly Robust Estimation

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Doubly robust estimation studies how to estimate causal effects, policy values, and other target quantities when some parts of a statistical model may be misspecified. Its central approach combines outcome regression with propensity or censoring weights, often using orthogonal scores and cross-fitting to limit bias from estimated nuisance models. Methods extend this framework to heterogeneous and distributional effects, longitudinal treatments, survival data, networks, and off-policy evaluation, with benchmarks ranging from randomized and observational studies to logged recommendations. A key tension is that robustness to one model’s misspecification does not remove the need for adequate data support and credible identification assumptions, while flexible models can still bring high variance or finite-sample instability.

Latest papers

35 papers9 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Doubly Robust Deep Kernel Tests for Structured Outcomes
Oct 27, 2026Robin Leman, Bariscan Bozkurt, Sofiane Tanji +2Two-Sample Testing Causal Inference
University College London · Gatsby Computational Neuroscience Unit, University College London · ICTEAM/INMA, UCLouvain
Doubly Robust Distributionally Robust Offline Contextual Pricing
Oct 22, 2026Min Xu, Xinyi Yin, Yunfan Zhang +3Off-Policy Evaluation Distributionally Robust Optimization
School of Management and Engineering, Nanjing University, Nanjing, China · School of Mathematics, Nanjing University, Nanjing, China · Stern School of Business, New York University, New York, USA
When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations
Oct 21, 2026Cong CaoCausal Effect Estimation Causal Mediation Analysis
Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA · Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
Doubly Robust Learning of Heterogeneous Survival Treatment Effects under Time-Varying Treatments
Oct 19, 2026Tomàs Garriga, Munib Mesinovic, Luke De Bretton-Gordon +3Causal Effect Estimation Individualized Treatment Effect Estimation
Novartis · Barcelona Supercomputing Center · Universitat Politècnica de Catalunya +1
Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes
Oct 14, 2026Dhanajit Brahma, Ruolin Meng, Lawrence Carin +1Proximal Causal Inference Causal Effect Estimation
Duke University
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
Peer Effects in Signed Networks: Separating Influence Through Positive and Negative Ties
Oct 2, 2026Xiaojing Du, Jiuyong Li, Lin Liu +3Causal Effect Estimation Causal Inference
Adelaide University, Adelaide, Australia · Hainan University, Haikou, China
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
Counterfactual Tool Ranking under Utility, Cost, and Privilege Constraints
Sep 19, 2026Jiapeng LiCounterfactual Evaluation Tool-Use Evaluation
Microsoft
Adaptive Doubly Robust Off-Policy Evaluation for Ranking Policies under Diverse User Behavior
Aug 30, 2026Kosuke Iguchi, Ren KishimotoOff-Policy Evaluation Doubly Robust Estimation
Institute of Science Tokyo Tokyo, Japan
When Interference Graphs Evolve: Doubly Robust Estimation of Dynamic Peer Effects
Aug 27, 2026Xiaojing DuCausal Effect Estimation Causal Inference
Adelaide University
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
Cost-optimal Sequential Testing via Doubly Robust Q-learning
Apr 13, 2026Doudou Zhou, Yiran Zhang, Dian Jin +3Q-Learning Cost-Sensitive Learning
Department of Statistics and Data Science, National University of Singapore · Public Health Sciences Division, Fred Hutch · Department of Biomedical Data Science, Stanford University School of Medicine +1
Orthogonal machine learning for conditional odds and risk ratios
Apr 12, 2026Jiacheng Ge, Iván DíazCausal Effect Estimation Doubly Robust Estimation
Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
Transfer Learning for Meta-analysis Under Covariate Shift
Apr 3, 2026Zilong Wang, Ali Abdeen, Turgay AyerCausal Inference Individualized Treatment Effect Estimation
Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
Decomposing Discrimination: Causal Mediation Analysis for AI-Driven Credit Decisions
Mar 29, 2026Duraimurugan 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
Off-Policy Evaluation and Learning for Survival Outcomes under Censoring
Mar 24, 2026Kohsuke Kubota, Mitsuhiro Takahashi, Yuta SaitoOff-Policy Evaluation Off-Policy RL
NTT DOCOMO, INC. · Hanjuku-kaso, Co, Ltd.

Oct 27, 2026J/K move · Enter open · S save

Doubly Robust Deep Kernel Tests for Structured Outcomes

Robin Leman, Bariscan Bozkurt, Sofiane Tanji, Arthur Gretton+1

University College London · Gatsby Computational Neuroscience Unit, University College London · ICTEAM/INMA, UCLouvain

Two-Sample Testing Causal Inference Deep Kernel Learning Doubly Robust Estimation

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We propose a test of whether two interventions induce the same outcome distribution when outcomes are structured and high-dimensional, such as images, text and graphs. We represent the interventional distributions as mean embeddings on learned deep features, such that the treatment effect is a doubly robust representation of the difference in mean embeddings. Our test, DR-KTE-Deep, optimises the learned features so as to maximise an estimated power criterion on this treatment effect representation. To motivate our learning objective, we show that the power criterion characterises the asymptotic signal-to-noise ratio of the test under fixed alternatives, and we derive a uniform finite-sample bound for its regularised estimate. We show that, with the learned kernel fixed, a held-out test is asymptotically valid under the null given suitable nuisance-estimation rates. Empirically, DR-KTE-Deep achieves substantially higher power than fixed and bandwidth-optimised kernel baselines across image, text and graph outcomes, while maintaining well-calibrated Type-I error.