Dose-Response Estimation

Latest papers

8 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Learning Deployable Causal Action Geometry under Temporal Non-Stationarity
Oct 12, 2026Changjian Liu, Tianyu Wang, Yuwei Xu +6Dose-Response Estimation Causal Effect Estimation
Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, China · Beijing Key Laboratory of Spatio-temporal Perception and Urban Resilience · Taobao & Tmall Group of Alibaba, Beijing, China +2
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
Predicting Quantifiability from Primary Screens to Prioritize Dose-Response Profiling
Aug 27, 2026Sean LimDose-Response Estimation Drug Discovery
Department of Natural Science, Rice University, Houston, TX, USA
Joint Flow Matching Enables Continuous Dose-Conditioned Cell Morphing
Aug 17, 2026Lea Bogensperger, Manuela Merlo, Martin Baumgartner +2Dose-Response Estimation Single-Cell Perturbation Modeling
University of Zurich · University Children’s Hospital Zurich
Interventional Flow Matching: Prospective Dose-Response Forecasting with Velocity-Field Jacobian Regularization
Jun 28, 2026Amirreza Dolatpour Fathkouhi, Justin Lee, Heman ShakeriFlow Matching Type 1 Diabetes
Department of Computer Science · School of Data Science
Attention-Based Estimation of the Individual Treatment Benefit Probability under Dose Variation
Jun 11, 2026Lev V. Utkin, Andrei V. Konstantinov, Stanislav K. Kogan +2Causal Effect Estimation Clinical Decision-Making
Peter the Great St.Petersburg Polytechnic University Higher School of Artificial Intelligence Technologies St.Petersburg, Russia
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
VIANA: character Value-enhanced Intensity Assessment via domain-informed Neural Architecture
Apr 1, 2026Luana P. Queiroz, Icaro S. C. Bernardes, Ana M. Ribeiro +2Dose-Response Estimation Molecular Representation Learning
LSRE-LCM, ALiCE, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal · Department of Chemical Engineering, Norwegian University of Science and Technology, Sem Sælands vei 4, NO-7491 Trondheim, Norway · Corporate Fragrance R&D, The Procter & Gamble Company, Ivorydale Innovation Center, 5299 Spring Grove Ave., Cincinnati, OH 45140, USA +1

Oct 12, 2026J/K move · Enter open · S save

Learning Deployable Causal Action Geometry under Temporal Non-Stationarity

Changjian Liu, Tianyu Wang, Yuwei Xu, Xiaoxuan Deng+5

Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing, China · Beijing Key Laboratory of Spatio-temporal Perception and Urban Resilience · Taobao & Tmall Group of Alibaba, Beijing, China+2

Dose-Response Estimation Causal Effect Estimation Policy Learning

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Learning a response law for continuous decisions from observational time series is central to many real-world decision systems, yet temporal non-stationarity poses a fundamental challenge: as outcomes drift over time, naively pooled models can conflate action effects with shifting baselines and even reverse action rankings. Even when the one-step response is identifiable, unrestricted cross-time generalization remains fundamentally ill-posed. We therefore seek the part of the response that remains stable for decisions even when outcome levels do not. We propose deployable causal action geometry: the action-response shape that remains after anchoring away action-independent level shifts on a selected link scale and profiling out positive amplitude variation. Building on this target, we develop a two-stage orthogonal estimator that first obtains cross-fitted pilot estimates of anchored responses and then performs honest profiled selection over link, representation, and shape classes. The resulting estimator admits oracle guarantees for the restricted target and, under approximate persistence, produces decision rules that remain near optimal in future periods. Experiments on synthetic data and Dominick's support the proposed target and its transfer behavior. In a randomized Taobao A/B test, the deployed system improves GMV and purchase counts by

1.16\%

and

1.70\%