Causal Effect Estimation

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Overview

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.

Latest papers

CardsList

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

  1. Observing the unobserved confounding through its effects: toward randomized trial-like estimates from real-world survival data

    Apr 13, 2026Vasiliki Stoumpou, Dimitris Bertsimas, Samuel Singer +1Causal Inference with Unobserved ConfoundingCausal Effect Estimation

    Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA · Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA · Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA +1

  2. Orthogonal machine learning for conditional odds and risk ratios

    Apr 12, 2026Jiacheng Ge, Iván DíazCausal Effect EstimationDoubly Robust Estimation

    Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA

  3. Identifying Causal Effects Using a Single Proxy Variable

    Apr 10, 2026Silvan Vollmer, Niklas Pfister, Sebastian WeichwaldCausal Effect EstimationCausal Inference with Unobserved Confounding

    Department of Mathematical Sciences University of Copenhagen Copenhagen, Denmark · Lakera AI Zurich, Switzerland

  4. A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling

    Apr 7, 2026Aman SinghUplift ModelingConditional Average Treatment Effect Estimation

  5. InferenceEvolve: Towards Automated Causal Effect Estimators through Self-Evolving AI

    Apr 5, 2026Can Wang, Hongyu Zhao, Yiqun ChenCausal Effect EstimationAutomated Algorithm Discovery

    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA · Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA

  6. Nonparametric Regression Discontinuity Designs with Survival Outcomes

    Apr 3, 2026Maximilian Schuessler, Erik Sverdrup, Robert Tibshirani +1Causal Effect EstimationSurvival Analysis

  7. Statistical realism is not evidence that LLMs can estimate treatment effects in social science experiments

    Apr 2, 2026Zonghan Li, Feng JiLarge Language Model-Based Social SimulationSynthetic Data Evaluation

    Department of Applied Psychology and Human Development, University of Toronto, ON, Canada

  8. The Digital Twin Counterfactual Framework: A Validation Architecture for Simulated Potential Outcomes

    Apr 1, 2026Olav LaudyCounterfactual EvaluationCausal Effect Estimation

  9. Orthogonal Learner for Estimating Heterogeneous Long-Term Treatment Effects

    Apr 1, 2026Haorui Ma, Dennis Frauen, Valentyn Melnychuk +1Causal Effect EstimationConditional Average Treatment Effect Estimation

    AI in Management, LMU Munich; Munich Center for Machine Learning; Munich, Germany

  10. Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap

    Apr 1, 2026Oscar Clivio, Alexander D'Amour, Alexander Franks +3Causal Effect EstimationCausal Representation Learning

    University of California, Berkeley

  11. Monodense Deep Neural Model for Determining Item Price Elasticity

    Mar 31, 2026Lakshya Garg, Sai Yaswanth, Deep Narayan Mishra +3Causal Effect EstimationFeedforward Neural Networks

    Walmart Inc. Hoboken,NJ, USA · Walmart Inc. Seattle,WA, USA · Walmart Inc. Sunnyvale,CA, USA +1

  12. Decomposing Discrimination: Causal Mediation Analysis for AI-Driven Credit Decisions

    Mar 29, 2026Duraimurugan RajamanickamFinancial ServicesCausal Effect Estimation

    VP, Artificial Intelligence, Hudson Valley Credit Union, Poughkeepsie, NY · PhD Candidate, Causal Machine Learning, University of Arkansas at Little Rock

  13. Maximin Learning of Individualized Treatment Effect on Multi-Domain Outcomes

    Mar 28, 2026Yuying Lu, Wenbo Fei, Yuanjia Wang +1Individualized Treatment Effect EstimationCausal Effect Estimation

    Department of Biostatistics, Columbia Mailman School of Public Health. · Department of Biostatistics, Peking University Health Science Center. · Beijing International Center for Mathematical Research, Peking University.

  14. Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis

    Mar 24, 2026Mohyeu Hussain, David Koblah, Reiner Dizon-Paradis +1Electronic Design AutomationCausal Interpretability

    University of Florida

  15. Improving RCT-Based Treatment Effect Estimation Under Covariate Mismatch via Calibrated Alignment

    Mar 19, 2026Amir Asiaee, Samhita PalCausal Effect EstimationCausal Representation Learning

    Department of Biostatistics, Vanderbilt University Medical Center, TN 37203, USA

  16. Conditional Distributional Treatment Effects: Doubly Robust Estimation and Testing

    Mar 17, 2026Saksham Jain, Alex LuedtkeCausal Effect EstimationCausal Inference

    Department of Statistics, University of Washington, Seattle, USA · Departments of Health Care Policy and Statistics, Harvard University, USA

  17. Beyond Means: Topological Causal Effects under Persistent-Homology Ignorability

    Mar 15, 2026Amir Saki, Usef FaghihiCausal Effect EstimationCausal Identifiability

    Université du Québec à Trois-Rivières (UQTR).

  18. Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference

    Mar 12, 2026Valentyn Melnychuk, Vahid Balazadeh, Stefan Feuerriegel +1Causal Effect EstimationPrior-Data Fitted Networks

    LMU Munich & Munich Center for Machine Learning (MCML), Munich, Germany · University of Toronto & Vector Institute, Toronto, Canada

  19. Causal Matrix Completion under Multiple Treatments via Mixed Synthetic Nearest Neighbors

    Mar 12, 2026Minrui Luo, Zhiheng ZhangCausal Effect EstimationMatrix Completion

    Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China · School of Statistics and Data Science, Shanghai University of Finance and Economics, Shanghai, China

  20. Causal Representation Learning with Optimal Compression under Complex Treatments

    Mar 12, 2026Wanting Liang, Haoang Chi, Zhiheng ZhangCausal Effect EstimationIndividualized Treatment Effect Estimation

    Shanghai University of Finance and Economics, Shanghai, China.

  21. CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Time

    Mar 12, 2026Nghia D. Nguyen, Pablo Robles-Granda, Lav R. VarshneyCausal Effect EstimationCounterfactual Learning

    Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign · VinUni-Illinois Smart Health Center, VinUniversity · AI Innovation Institute, Stony Brook University +1

  22. Interventional Time Series Priors for Causal Foundation Models

    Mar 11, 2026Dennis Thumm, Ying ChenCausal Foundation ModelsSynthetic Data Generation

    National University of Singapore · Department of Mathematics, Centre for Quantitative Finance, Risk Management Institute, National University of Singapore

  23. Structural Causal Bottleneck Models

    Mar 9, 2026Simon Bing, Jonas Wahl, Jakob RungeStructural Causal ModelsCausal Effect Estimation

    Equal contribution · Technische Universität Berlin · Department of Computer Science, University of Potsdam +1

  24. Unifying On- and Off-Policy Variance Reduction Methods

    Mar 9, 2026Olivier JeunenCausal Effect EstimationA/b Testing

    aampe Antwerp, Belgium

  25. How Wrong Can Your Counterfactual Be? Quantifying Confounding Bias for Continuous Treatments without a Control Group

    Mar 8, 2026Yu Wang, Xiangchen Liu, Siguang LiCausal Effect EstimationCausal Inference

    Department of Economics, Cornell University. · Department of Family and Consumer Sciences, California State University, Long Beach. · Society Hub, Hong Kong University of Science and Technology (Guangzhou).

  26. TEA-Time: Transporting Effects Across Time

    Mar 7, 2026Harsh Parikh, Gabriel Levin-Konigsberg, Dominique Perrault-Joncas +1Causal Effect Estimation

    Amazon SCOT, Seattle, USA · Yale University, New Haven, USA · Duke University, Durham, USA

  27. Observationally Informed Adaptive Causal Experimental Design

    Mar 4, 2026Erdun Gao, Liang Zhang, Jake Fawkes +5Active Experimental DesignCausal Inference

    Australian Institute for Machine Learning, Adelaide University · Hong Kong University of Science and Technology (Guangzhou) · University College London +3

  28. Generalized Bayes for Causal Inference

    Mar 3, 2026Emil Javurek, Dennis Frauen, Yuxin Wang +1Generalized Bayesian InferenceCausal Inference

    LMU Munich · Munich Center for Machine Learning

  29. Estimating Visual Attribute Effects in Advertising from Observational Data: A Deepfake-Informed Double Machine Learning Approach

    Mar 2, 2026Yizhi Liu, Balaji Padmanabhan, Siva ViswanathanCausal Effect EstimationCausal Inference

    Robert H. Smith School of Business, University of Maryland, College Park, MD, USA

  30. De-paradox Tree: Breaking Down Simpson's Paradox via A Kernel-Based Partition Algorithm

    Mar 2, 2026Xian Teng, Yu-Ru LinCausal InferenceCausal Reasoning

    University of Pittsburgh, Pittsburgh, Pennsylvania, USA