{"list":"other","total":62,"rows":[{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1709.07871","title":"FiLM: Visual Reasoning with a General Conditioning Layer","created":"2017-09-22","cited_by_count":2333},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1609.09106","title":"HyperNetworks","created":"2016-09-27","cited_by_count":1027},{"id":"1906.02691","title":"An Introduction to Variational Autoencoders","created":"2019-06-06","cited_by_count":1000},{"id":"1705.07057","title":"Masked Autoregressive Flow for Density Estimation","created":"2017-05-19","cited_by_count":684},{"id":"1906.04032","title":"Neural Spline Flows","created":"2019-06-10","cited_by_count":596},{"id":"1706.03461","title":"Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning","created":"2017-06-12","cited_by_count":478},{"id":"1705.08821","title":"Causal Effect Inference with Deep Latent-Variable Models","created":"2017-05-24","cited_by_count":410},{"id":"1712.04912","title":"Quasi-Oracle Estimation of Heterogeneous Treatment Effects","created":"2017-12-13","cited_by_count":332},{"id":"1605.03661","title":"Learning Representations for Counterfactual Inference","created":"2016-05-12","cited_by_count":329},{"id":"1502.03509","title":"MADE: Masked Autoencoder for Distribution Estimation","created":"2015-02-12","cited_by_count":307},{"id":"2006.06485","title":"Deep Structural Causal Models for Tractable Counterfactual Inference","created":"2020-06-11","cited_by_count":211},{"id":"1804.00779","title":"Neural Autoregressive Flows","created":"2018-04-03","cited_by_count":208},{"id":"1704.02801","title":"Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes","created":"2017-04-10","cited_by_count":158},{"id":"1709.02023","title":"CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training","created":"2017-09-06","cited_by_count":152},{"id":"1901.09036","title":"Orthogonal Statistical Learning","created":"2019-01-25","cited_by_count":151},{"id":"2101.10943","title":"Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms","created":"2021-01-26","cited_by_count":144},{"id":"2203.06469","title":"Semiparametric doubly robust targeted double machine learning: a review","created":"2022-03-12","cited_by_count":133},{"id":"2101.06046","title":"Counterfactual Generative Networks","created":"2021-01-15","cited_by_count":111},{"id":"1707.02641","title":"Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition","created":"2017-07-09","cited_by_count":106},{"id":"2202.10166","title":"Diffusion Causal Models for Counterfactual Estimation","created":"2022-02-21","cited_by_count":87},{"id":"2410.08770","title":"Causal machine learning for predicting treatment outcomes","created":"2024-10-11","cited_by_count":74},{"id":"2001.07426","title":"Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects","created":"2020-01-21","cited_by_count":72},{"id":"2001.10652","title":"Treatment effect estimation with disentangled latent factors","created":"2020-01-29","cited_by_count":63},{"id":"1802.04908","title":"Conditional Density Estimation with Bayesian Normalising Flows","created":"2018-02-14","cited_by_count":54},{"id":"2306.15764","title":"High Fidelity Image Counterfactuals with Probabilistic Causal Models","created":"2023-06-27","cited_by_count":53},{"id":"2103.04850","title":"Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding","created":"2021-03-08","cited_by_count":50},{"id":"2010.12618","title":"Counterfactual Representation Learning with Balancing Weights","created":"2020-10-23","cited_by_count":45},{"id":"2102.12034","title":"Semiparametric counterfactual density estimation","created":"2021-02-24","cited_by_count":42},{"id":"2102.08208","title":"Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression","created":"2021-02-16","cited_by_count":40},{"id":"2202.12263","title":"Causal Effect Identification in Cluster DAGs","created":"2022-02-22","cited_by_count":36},{"id":"2310.17463","title":"Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation","created":"2023-10-26","cited_by_count":26},{"id":"1907.08982","title":"Noise Regularization for Conditional Density Estimation","created":"2019-07-21","cited_by_count":23},{"id":"2310.11011","title":"From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling","created":"2023-10-17","cited_by_count":22},{"id":"2209.06203","title":"Normalizing Flows for Interventional Density Estimation","created":"2022-09-13","cited_by_count":21},{"id":"2401.02602","title":"Neural Causal Abstractions","created":"2024-01-05","cited_by_count":21},{"id":"2407.05287","title":"Model-agnostic meta-learners for estimating heterogeneous treatment effects over time","created":"2024-07-07","cited_by_count":19},{"id":"2205.11486","title":"Robust and Agnostic Learning of Conditional Distributional Treatment Effects","created":"2022-05-23","cited_by_count":18},{"id":"2305.15742","title":"Counterfactual Generative Models for Time-Varying Treatments","created":"2023-05-25","cited_by_count":17},{"id":"2410.08924","title":"DiffPO: A causal diffusion model for learning distributions of potential outcomes","created":"2024-10-11","cited_by_count":17},{"id":"1806.02935","title":"Causal effects based on distributional distances","created":"2018-06-08","cited_by_count":16},{"id":"2201.09874","title":"CVAE-H: Conditionalizing Variational Autoencoders via Hypernetworks and Trajectory Forecasting for Autonomous Driving","created":"2022-01-24","cited_by_count":13},{"id":"2305.04116","title":"The Fundamental Limits of Structure-Agnostic Functional Estimation","created":"2023-05-06","cited_by_count":12},{"id":"2303.16711","title":"One-Step Estimation of Differentiable Hilbert-Valued Parameters","created":"2023-03-29","cited_by_count":11},{"id":"2304.13237","title":"An Efficient Doubly-Robust Test for the Kernel Treatment Effect","created":"2023-04-26","cited_by_count":10},{"id":"2210.00471","title":"OCD: Learning to Overfit with Conditional Diffusion Models","created":"2022-10-02","cited_by_count":9},{"id":"2110.01664","title":"Estimating Potential Outcome Distributions with Collaborating Causal Networks","created":"2021-10-04","cited_by_count":8},{"id":"2505.13092","title":"Treatment Effect Estimation for Optimal Decision-Making","created":"2025-05-19","cited_by_count":8},{"id":"2206.00516","title":"Feature Selection for Discovering Distributional Treatment Effect Modifiers","created":"2022-06-01","cited_by_count":7},{"id":"2309.16129","title":"Counterfactual Density Estimation using Kernel Stein Discrepancies","created":"2023-09-28","cited_by_count":7},{"id":"2311.09423","title":"Orthogonal prediction of counterfactual outcomes","created":"2023-11-15","cited_by_count":7},{"id":"2402.14264","title":"Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation","created":"2024-02-22","cited_by_count":6},{"id":"2506.05945","title":"On Efficient Estimation of Distributional Treatment Effects under Covariate-Adaptive Randomization","created":"2025-06-06","cited_by_count":6},{"id":"2411.08778","title":"Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest","created":"2024-11-13","cited_by_count":5},{"id":"2112.02219","title":"Transferring Unconditional to Conditional GANs with Hyper-Modulation","created":"2021-12-04","cited_by_count":4},{"id":"2310.12757","title":"Conservative Inference for Counterfactuals","created":"2023-10-19","cited_by_count":4},{"id":"2410.02025","title":"A Likelihood Based Approach to Distribution Regression Using Conditional Deep Generative Models","created":"2024-10-02","cited_by_count":4},{"id":"2506.14329","title":"Adjustment for Confounding using Pre-Trained Representations","created":"2025-06-17","cited_by_count":4},{"id":"2508.13355","title":"Counterfactual Probabilistic Diffusion with Expert Models","created":"2025-08-18","cited_by_count":4},{"id":"2403.19917","title":"Doubly robust estimation and inference for a log-concave counterfactual density","created":"2024-03-29","cited_by_count":2}],"next_cursor":null}