{"total":144,"rows":[{"id":"2204.07258","title":"Causal Transformer for Estimating Counterfactual Outcomes","created":"2022-04-14","cited_by_count":123,"area":null},{"id":"2106.03765","title":"On Inductive Biases for Heterogeneous Treatment Effect Estimation","created":"2021-06-07","cited_by_count":75,"area":null},{"id":"2410.08770","title":"Causal machine learning for predicting treatment outcomes","created":"2024-10-11","cited_by_count":74,"area":null},{"id":"2205.11402","title":"Causal Machine Learning for Healthcare and Precision Medicine","created":"2022-05-23","cited_by_count":68,"area":null},{"id":"2506.07918","title":"CausalPFN: Amortized Causal Effect Estimation via In-Context Learning","created":"2025-06-09","cited_by_count":49,"area":null},{"id":"2106.01939","title":"Causal Effect Inference for Structured Treatments","created":"2021-06-03","cited_by_count":39,"area":null},{"id":"2202.12891","title":"Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects","created":"2022-02-25","cited_by_count":32,"area":null},{"id":"2506.10914","title":"Foundation Models for Causal Inference via Prior-Data Fitted Networks","created":"2025-06-12","cited_by_count":32,"area":null},{"id":"2302.02923","title":"In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation","created":"2023-02-06","cited_by_count":29,"area":null},{"id":"2310.17463","title":"Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation","created":"2023-10-26","cited_by_count":26,"area":null},{"id":"2110.14001","title":"SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data","created":"2021-10-26","cited_by_count":25,"area":null},{"id":"2202.01336","title":"Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation","created":"2022-02-02","cited_by_count":25,"area":null},{"id":"2211.01939","title":"Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation","created":"2022-11-03","cited_by_count":25,"area":null},{"id":"2306.00315","title":"Explicit Feature Interaction-aware Uplift Network for Online Marketing","created":"2023-06-01","cited_by_count":25,"area":null},{"id":"2104.09323","title":"Sequential Deconfounding for Causal Inference with Unobserved Confounders","created":"2021-04-16","cited_by_count":24,"area":null},{"id":"2305.16988","title":"Sharp Bounds for Generalized Causal Sensitivity Analysis","created":"2023-05-26","cited_by_count":24,"area":null},{"id":"2209.06203","title":"Normalizing Flows for Interventional Density Estimation","created":"2022-09-13","cited_by_count":21,"area":null},{"id":"2210.06183","title":"Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation","created":"2022-10-08","cited_by_count":21,"area":null},{"id":"2311.11321","title":"Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation","created":"2023-11-19","cited_by_count":20,"area":null},{"id":"2407.05287","title":"Model-agnostic meta-learners for estimating heterogeneous treatment effects over time","created":"2024-07-07","cited_by_count":19,"area":null},{"id":"2208.08544","title":"Estimating individual treatment effects under unobserved confounding using binary instruments","created":"2022-08-17","cited_by_count":18,"area":null},{"id":"2301.12292","title":"Zero-shot causal learning","created":"2023-01-28","cited_by_count":17,"area":null},{"id":"2311.16026","title":"A Neural Framework for Generalized Causal Sensitivity Analysis","created":"2023-11-27","cited_by_count":17,"area":null},{"id":"2410.08924","title":"DiffPO: A causal diffusion model for learning distributions of potential outcomes","created":"2024-10-11","cited_by_count":17,"area":"diffusion"},{"id":"2206.08363","title":"Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability","created":"2022-06-16","cited_by_count":15,"area":null},{"id":"2306.04255","title":"Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time","created":"2023-06-07","cited_by_count":15,"area":null},{"id":"2306.01424","title":"Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Model","created":"2023-06-02","cited_by_count":14,"area":null},{"id":"2303.08516","title":"Fair Off-Policy Learning from Observational Data","created":"2023-03-15","cited_by_count":11,"area":null},{"id":"2305.15984","title":"Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation","created":"2023-05-25","cited_by_count":11,"area":null},{"id":"2406.02464","title":"Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments","created":"2024-06-04","cited_by_count":11,"area":null},{"id":"2204.06251","title":"Experimental Standards for Deep Learning in Natural Language Processing Research","created":"2022-04-13","cited_by_count":10,"area":null},{"id":"2205.14714","title":"Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects","created":"2022-05-29","cited_by_count":10,"area":null},{"id":"2209.08860","title":"A Survey of Deep Causal Models and Their Industrial Applications","created":"2022-09-19","cited_by_count":10,"area":null},{"id":"2405.12387","title":"Conformal Counterfactual Inference under Hidden Confounding","created":"2024-05-20","cited_by_count":10,"area":null},{"id":"2411.03387","title":"Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner","created":"2024-11-05","cited_by_count":10,"area":null},{"id":"2505.13072","title":"Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data","created":"2025-05-19","cited_by_count":10,"area":null},{"id":"2202.02096","title":"To Impute or not to Impute? Missing Data in Treatment Effect Estimation","created":"2022-02-04","cited_by_count":9,"area":null},{"id":"2205.14692","title":"Generalization bounds and algorithms for estimating conditional average treatment effect of dosage","created":"2022-05-29","cited_by_count":9,"area":null},{"id":"2210.10530","title":"Adversarial De-confounding in Individualised Treatment Effects Estimation","created":"2022-10-19","cited_by_count":9,"area":null},{"id":"2111.08656","title":"Causal Effect Variational Autoencoder with Uniform Treatment","created":"2021-11-16","cited_by_count":8,"area":null},{"id":"2402.04906","title":"Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects","created":"2024-02-07","cited_by_count":8,"area":null},{"id":"2505.13092","title":"Treatment Effect Estimation for Optimal Decision-Making","created":"2025-05-19","cited_by_count":8,"area":null},{"id":"2108.03039","title":"Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects","created":"2021-08-06","cited_by_count":7,"area":null},{"id":"2410.03514","title":"Stabilized Neural Prediction of Potential Outcomes in Continuous Time","created":"2024-10-04","cited_by_count":7,"area":null},{"id":"2203.00554","title":"Neural Score Matching for High-Dimensional Causal Inference","created":"2022-03-01","cited_by_count":6,"area":null},{"id":"2401.16986","title":"Causal Machine Learning for Cost-Effective Allocation of Development Aid","created":"2024-01-30","cited_by_count":6,"area":null},{"id":"2412.11511","title":"Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets","created":"2024-12-16","cited_by_count":6,"area":null},{"id":"2302.12718","title":"Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data","created":"2023-02-23","cited_by_count":5,"area":null},{"id":"2403.03791","title":"KG-TREAT: Pre-training for Treatment Effect Estimation by Synergizing Patient Data with Knowledge Graphs","created":"2024-03-06","cited_by_count":5,"area":null},{"id":"2412.02000","title":"Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptation","created":"2024-12-02","cited_by_count":5,"area":null},{"id":"2412.11136","title":"Minimax Regret Estimation for Generalizing Heterogeneous Treatment Effects with Multisite Data","created":"2024-12-15","cited_by_count":5,"area":null},{"id":"2502.04274","title":"Orthogonal Representation Learning for Estimating Causal Quantities","created":"2025-02-06","cited_by_count":5,"area":null},{"id":"2503.03486","title":"Differentially Private Learners for Heterogeneous Treatment Effects","created":"2025-03-05","cited_by_count":5,"area":null},{"id":"2107.13346","title":"Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators","created":"2021-07-28","cited_by_count":4,"area":null},{"id":"2109.12769","title":"Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark","created":"2021-09-27","cited_by_count":4,"area":null},{"id":"2201.12692","title":"Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance","created":"2022-01-30","cited_by_count":4,"area":null},{"id":"2202.09096","title":"A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics","created":"2022-02-18","cited_by_count":4,"area":null},{"id":"2207.09139","title":"Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression","created":"2022-07-19","cited_by_count":4,"area":null},{"id":"2401.11263","title":"Estimating Heterogeneous Treatment Effects on Survival Outcomes Using Counterfactual Censoring Unbiased Transformations","created":"2024-01-20","cited_by_count":4,"area":null},{"id":"2411.08019","title":"Language Models as Causal Effect Generators","created":"2024-11-12","cited_by_count":4,"area":null},{"id":"2503.14795","title":"The Hardness of Validating Observational Studies with Experimental Data","created":"2025-03-19","cited_by_count":4,"area":null},{"id":"2506.13680","title":"Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects","created":"2025-06-16","cited_by_count":4,"area":null},{"id":"2508.13355","title":"Counterfactual Probabilistic Diffusion with Expert Models","created":"2025-08-18","cited_by_count":4,"area":null},{"id":"2510.18768","title":"Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference","created":"2025-10-21","cited_by_count":4,"area":null},{"id":"2202.02416","title":"Generalized Causal Tree for Uplift Modeling","created":"2022-02-04","cited_by_count":3,"area":null},{"id":"2211.07264","title":"Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment","created":"2022-11-14","cited_by_count":3,"area":null},{"id":"2305.09126","title":"Transfer Learning for Causal Effect Estimation","created":"2023-05-16","cited_by_count":3,"area":null},{"id":"2307.03315","title":"Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation","created":"2023-07-06","cited_by_count":3,"area":null},{"id":"2307.09700","title":"The Connection Between R-Learning and Inverse-Variance Weighting for Estimation of Heterogeneous Treatment Effects","created":"2023-07-19","cited_by_count":3,"area":null},{"id":"2309.12036","title":"Uplift vs. predictive modeling: a theoretical analysis","created":"2023-09-21","cited_by_count":3,"area":null},{"id":"2405.09516","title":"Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis","created":"2024-05-15","cited_by_count":3,"area":null},{"id":"2406.00335","title":"Benchmarking for Deep Uplift Modeling in Online Marketing","created":"2024-06-01","cited_by_count":3,"area":null},{"id":"2407.01065","title":"Improve ROI with Causal Learning and Conformal Prediction","created":"2024-07-01","cited_by_count":3,"area":null},{"id":"2408.13002","title":"Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence","created":"2024-08-23","cited_by_count":3,"area":null},{"id":"2408.17053","title":"Estimating Conditional Average Treatment Effects via Sufficient Representation Learning","created":"2024-08-30","cited_by_count":3,"area":null},{"id":"2410.08816","title":"Uncertainty-Aware Optimal Treatment Selection for Clinical Time Series","created":"2024-10-11","cited_by_count":3,"area":null},{"id":"2412.04799","title":"Estimating the treatment effect over time under general interference through deep learner integrated TMLE","created":"2024-12-06","cited_by_count":3,"area":null},{"id":"2505.16051","title":"Flow-based Generative Modeling of Potential Outcomes and Counterfactuals","created":"2025-05-21","cited_by_count":3,"area":null},{"id":"2510.19643","title":"Overlap-weighted orthogonal meta-learner for treatment effect estimation over time","created":"2025-10-22","cited_by_count":3,"area":null},{"id":"2602.03517","title":"Rank-Learner: Orthogonal Ranking of Treatment Effects","created":"2026-02-03","cited_by_count":3,"area":null},{"id":"2305.11353","title":"Meta-learning for heterogeneous treatment effect estimation with closed-form solvers","created":"2023-05-19","cited_by_count":2,"area":null},{"id":"2310.16819","title":"CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression","created":"2023-10-25","cited_by_count":2,"area":null},{"id":"2402.17705","title":"Federated Learning for Estimating Heterogeneous Treatment Effects","created":"2024-02-27","cited_by_count":2,"area":null},{"id":"2402.18392","title":"Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators","created":"2024-02-28","cited_by_count":2,"area":null},{"id":"2403.00694","title":"Defining Expertise: Applications to Treatment Effect Estimation","created":"2024-03-01","cited_by_count":2,"area":null},{"id":"2404.17483","title":"Differentiable Pareto-Smoothed Weighting for High-Dimensional Heterogeneous Treatment Effect Estimation","created":"2024-04-26","cited_by_count":2,"area":null},{"id":"2502.18960","title":"Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination","created":"2025-02-26","cited_by_count":2,"area":null},{"id":"2502.18994","title":"Long-term Causal Inference via Modeling Sequential Latent Confounding","created":"2025-02-26","cited_by_count":2,"area":null},{"id":"2505.21641","title":"PrivATE: Differentially Private Confidence Intervals for Average Treatment Effects","created":"2025-05-27","cited_by_count":2,"area":null},{"id":"2507.02843","title":"LLM-Driven Treatment Effect Estimation Under Inference Time Text Confounding","created":"2025-07-03","cited_by_count":2,"area":null},{"id":"2509.22467","title":"CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks","created":"2025-09-26","cited_by_count":2,"area":null},{"id":"2511.14545","title":"DeepBlip: Estimating Conditional Average Treatment Effects Over Time","created":"2025-11-18","cited_by_count":2,"area":null},{"id":"2605.25998","title":"Causal methods for LLM development and evaluation","created":"2026-05-25","cited_by_count":2,"area":"alignment"},{"id":"2211.04370","title":"NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation","created":"2022-11-08","cited_by_count":1,"area":null},{"id":"2211.10793","title":"BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect","created":"2022-11-19","cited_by_count":1,"area":null},{"id":"2310.03258","title":"Assessing Electricity Service Unfairness with Transfer Counterfactual Learning","created":"2023-10-05","cited_by_count":1,"area":null},{"id":"2311.03002","title":"Estimating treatment effects from single-arm trials via latent-variable modeling","created":"2023-11-06","cited_by_count":1,"area":null},{"id":"2402.12190","title":"Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data","created":"2024-02-19","cited_by_count":1,"area":null},{"id":"2409.04407","title":"Exploiting Missing Data Remediation Strategies using Adversarial Missingness Attacks","created":"2024-09-06","cited_by_count":1,"area":null},{"id":"2409.04500","title":"Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm","created":"2024-09-06","cited_by_count":1,"area":null}],"next_cursor":"100"}