Causal Effect Estimation

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Causal effect estimation asks what would happen to outcomes if people, policies, or systems received different treatments, including average, subgroup-specific, and dose-response effects. Researchers use randomized trials and quasi-experiments alongside observational methods such as matching, propensity weighting, g-computation, instrumental variables, synthetic controls, and doubly robust or debiased machine learning. Current work extends these tools to longitudinal decisions, networks with spillovers, multiple or continuous treatments, and effects on entire outcome distributions, while improving uncertainty estimates and experimental design. A central challenge is that flexible prediction cannot by itself resolve unmeasured confounding, weak overlap, or violated transport assumptions, so credible estimates depend on identification assumptions and careful validation.

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266 papers35 papers in the last four weeks, up 35% on the four weeks before. 0.2% of all new papers.

When Does Pre-Trained Capacity Help Causal Learning? From Scalar Prediction to Counterfactual Post-Training
Oct 23, 2026Qi Xu, Zijie Lei, Zhifei Deng +2Causal Inference Causal Effect Estimation
Meta Platforms, Inc., Sunnyvale, CA, USA
Sharp Bounds on Continuous Treatment Effects under Unobserved Confounding
Oct 23, 2026Junzhe ZhangCausal Effect Estimation Causal Inference with Unobserved Confounding
Syracuse University
A Foundation Model for Policy In-Context Longitudinal Causal Effect Estimation
Oct 23, 2026Wenxin Chen, Kyra Gan, Mingyu Lu +5Causal Effect Estimation Causal Foundation Models
Cornell University, New York City, NY · University of Washington, Seattle, WA · Columbia University, New York City, NY +2
Forecasting Associations, Not Interventions: A Preregistered Causal Audit of Covariate-Aware Time Series Foundation Models
Oct 22, 2026Zong-Han BaiTime-Series Foundation Models Causal Effect Estimation
National Tsing Hua University, Hsinchu, Taiwan.
PriMa-Causa: Causal Prescriptive Maintenance
Oct 22, 2026Felix SaretzkyCausal Inference Causal Effect Estimation
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
Backtracking Direct, Indirect and Total Effects
Oct 20, 2026Jose M. PeñaCausal Effect Estimation Causal Mediation Analysis
STIMA, IDA, LiU
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
TaQFormer: A Treatment-as-Query Transformer with Data-Fusion for Multidimensional Treatment Modeling in Coupon Allocation
Oct 19, 2026Jiahong Li, Zeqin Yang, Yu Xiang +8Individualized Treatment Effect Estimation Causal Effect Estimation
Didi Chuxing Beijing, China
Conditional Average Treatment Effect Distillation for Hypothetical State-Intervention Targeting Under Budget Constraints
Oct 19, 2026Vasundhara Acharya, Bülent YenerPolicy Learning Causal Effect Estimation
Rensselaer Polytechnic Institute, Troy, USA
Estimating Causal Bounds from Observational Data With Neural Generative Models
Oct 18, 2026Max Zhu, Srivathsan Amruth, Sina Akbari +1Causal Inference Causal Effect Estimation
Department of Pure Mathematics and Mathematical Statistics University of Cambridge
Graph-Regularized Synthetic Control for Stable Counterfactual Estimation under Donor Redundancy
Oct 16, 2026Yanlin Zhang, Sungyong Chung, Alireza TalebpourSynthetic Control Causal Effect Estimation
University of Illinois Urbana-Champaign
DeDiCa: A Scalable Deconfounding Discrete Causal Generative Model
Oct 16, 2026Kunchangtai Liang, Adrián Javaloy, Antonio Vergari +1Causal Inference Causal Effect Estimation
Department of Computer Science, Saarland University, Saarbrücken, Germany · Department of Computer Science, University of Bath, Bath, United Kingdom · School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
Unit-level aggregation breaks coverage in causal foundation models
Oct 16, 2026Gabe Schulman, Lucius E. J. Bynum, Yoav WaldCausal Foundation Models Causal Effect Estimation
New York University · Technion
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
HiSTOR: Structured Treatment-outcome Representation Learning for High-Dimensional Longitudinal Data
Oct 13, 2026Chuanqi Lao, Junzhou Huang, Qiao LiuCausal Effect Estimation Individualized Treatment Effect Estimation
Department of Computer Science and Engineering, The University of Texas at Arlington · Department of Biostatistics, Yale School of Public Health, Yale University
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
Explaining Heterogeneous Treatment Effects with Causal Forests and the Hoeffding Functional Decomposition
Oct 12, 2026Gaëtan Patinier, Clément Bénard, Margaux TörnqvistCausal Effect Estimation Causal Interpretability
Theremia · Thales cortAIx-Labs
Estimating the Causal Impact of Social Navigation Signals in Corridor Scenarios
Oct 12, 2026Francesco Baldo, Luca Castri, Gloria Beraldo +1Social Robot Navigation Social Navigation
Department of Information Engineering, University of Padua · Institute of Cognitive Sciences and Technologies, National Research Council of Italy
Prediction-Powered Data Fusion for Treatment Effect Estimation
Oct 8, 2026Yonghan Jung, Shu YangCausal Effect Estimation Conditional Average Treatment Effect Estimation
University of Illinois Urbana-Champaign · North Carolina State University
Bayesian Causal Forests for Zero-Inflated Continuous Outcomes
Oct 8, 2026Hugo Gobato Souto, Eric Wang, Igor ShuryakCausal Effect Estimation Conditional Average Treatment Effect Estimation
University of São Paulo, Brazil. · Columbia University Irving Medical Center.
Beyond Distributional Fidelity: Causal-Penalized Diffusion for Synthetic Tabular Data
Oct 8, 2026Lan Tao, Yongxian He, Shirong Xu +2Synthetic Tabular Data Generation Tabular Diffusion Models
Department of Statistics & Data Science, University of California, Los Angeles · Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego · Wang Yanan Institute for Studies in Economics, Xiamen 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
Policy Learning with Weak Signals
Oct 7, 2026Benedikt Koch, Winston Chou, Aurélien Bibaut +1Policy Learning Causal Effect Estimation
Harvard University · Netflix
Symmetry-Informed Causal Partial Identification
Oct 6, 2026Uzair Akbar, Zulfiqar Zaidi, Niki Kilbertus +2Causal Inference Causal Effect Estimation
Georgia Tech · TU Munich · Helmholtz Munich +2
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
Incentive Alignment in Online Experimentation
Oct 5, 2026Ermis Soumalias, Richard Mudd, Abbas ZaidiCausal Effect Estimation A/b Testing
Meta Platforms, Inc.
Proximal Causal Learning under Unmeasured Confounding
Oct 3, 2026Ying Tang, Yi WangProximal Causal Inference Causal Effect Estimation
Shanghai University of International Business and Economics, Shanghai 201620, China
Invariance of Clustering Operations in Causal Effect Identification
Oct 2, 2026Jani Nykänen, Otto Tabell, Santtu Tikka +1Causal Identifiability Causal Effect Estimation
Department of Mathematics and Statistics FI-40014 University of Jyvaskyla, Finland
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

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When Does Pre-Trained Capacity Help Causal Learning? From Scalar Prediction to Counterfactual Post-Training

Qi Xu, Zijie Lei, Zhifei Deng, Zhigang Hua+1

Meta Platforms, Inc., Sunnyvale, CA, USA

Causal Inference Causal Effect Estimation Language Model Post-Training Counterfactual Reasoning in Language Models Language Model Scaling

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How does pre-trained model capacity relate to performance after adaptation for causal tasks? We study two systems built on the Qwen 2.5 family: CausalLLM, which estimates scalar potential outcomes with treatment-specific heads, and CausalGen, which generates treatment-conditioned text or code using soft prefixes and factual supervised fine-tuning (SFT), counterfactual SFT, and preference alignment. Increasing backbone size from 0.5B to 1.5B yields mixed scalar-estimation results on IHDP, but improves CodeGen pass@1 from 0.267 to 0.575 and treatment-effect score from 0.231 to 0.515 after the complete generative pipeline. Within 0.5B CausalLLM, narrative inputs reduce mean sqrt(PEHE) from 1.842 to 1.780, a 3.4% improvement over tabular inputs; the 5.8% improvement over a no-LLM baseline combines changes in representation and input format. These descriptive comparisons motivate a distinction between available and convertible capacity. They do not isolate the contribution of post-training: the two systems differ in dataset, architecture, output modality, and supervision, and intermediate-stage evaluations are unavailable. With only two small backbones and three replications, the results support a hypothesis about capacity--adaptation alignment rather than a general scaling law or a demonstrated causal mechanism.