A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation | Paper Citation Record

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This paper cites Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queries

Reference 1

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation

Reference 2

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation On Inductive Biases for Heterogeneous Treatment Effect Estimation

Reference 3

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Nonparametric estimation of heterogeneous treatment effects: From theory to learning algorithms

Reference 4

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This paper cites A First Course in Causal Inference.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation A First Course in Causal Inference

Reference 5

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Tweedie’s formula and selection bias

Reference 6

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Unresolved cited work

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Card: Classification and regression diffusion models

Reference 8

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This paper cites Generalizing the intention-to-treat effect of an active control against placebo from historical placebo-controlled trials to an active-controlled trial: A case study of the efficacy of daily oral TDF/FTC in the HPTN 084 study.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Generalizing the intention-to-treat effect of an active control against placebo from historical placebo-controlled trials to an active-controlled trial: A case study of the efficacy of daily oral TDF/FTC in the HPTN 084 study

Reference 9

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Denoising Diffusion Probabilistic Models

Reference 10

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Unresolved cited work

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Imbens and Donald B

Reference 12

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This paper cites D eep M atch: Balancing deep covariate representations for causal inference using adversarial training.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation D eep M atch: Balancing deep covariate representations for causal inference using adversarial training

Reference 13

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This paper cites Elucidating the design space of diffusion-based generative models.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Elucidating the design space of diffusion-based generative models

Reference 14

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Towards optimal doubly robust estimation of heterogeneous causal effects

Reference 15

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models

Reference 16

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This paper cites Künzel, Jasjeet S.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Künzel, Jasjeet S

Reference 17

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This paper cites Variance reduction in the inverse probability weighted estimators for the average treatment effect using the propensity score.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Variance reduction in the inverse probability weighted estimators for the average treatment effect using the propensity score

Reference 18

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This paper cites Causal modeling with stationary diffusions.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Causal modeling with stationary diffusions

Reference 19

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Diff-I nstruct: A universal approach for transferring knowledge from pre-trained diffusion models

Reference 20

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models

Reference 21

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Diff PO : A causal diffusion model for learning distributions of potential outcomes

Reference 22

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation

Reference 23

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Diffusion Based Causal Representation Learning

Reference 24

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Causality: Models, Reasoning and Inference

Reference 25

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Trygve haavelmo and the emergence of causal calculus

Reference 26

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation DreamFusion: Text-to-3D using 2D Diffusion

Reference 27

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation An empirical B ayes approach to statistics

Reference 28

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This paper cites Robins, Andrea Rotnitzky, and Lue Ping Zhao.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Robins, Andrea Rotnitzky, and Lue Ping Zhao

Reference 29

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Rosenbaum and Donald B

Reference 30

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Unresolved cited work

Reference 31

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Diffusion Causal Models for Counterfactual Estimation

Reference 32

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Diffusion Models for Causal Discovery via Topological Ordering

Reference 33

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Johansson, and David Sontag

Reference 34

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Diffusion Model in Causal Inference with Unmeasured Confounders

Reference 35

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Deep Unsupervised Learning using Nonequilibrium Thermodynamics

Reference 36

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Generative M odeling by E stimating G radients of the D ata D istribution

Reference 37

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Score-Based Generative Modeling through Stochastic Differential Equations

Reference 38

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Unresolved cited work

Reference 39

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Covariate shift adaptation by importance weighted cross validation

Reference 40

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation General identifiability and achievability for causal representation learning

Reference 41

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation A connection between score matching and denoising autoencoders

Reference 42

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Diffusion-GAN : Training GANs with diffusion

Reference 43

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Prolific D reamer: High-fidelity and diverse text-to- 3D generation with variational score distillation, 2023 a

Reference 44

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation

Reference 45

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Semi-implicit variational inference

Reference 46

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation One-step diffusion with distribution matching distillation

Reference 47

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation GANITE : Estimation of individualized treatment effects using generative adversarial nets

Reference 48

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Hierarchical semi-implicit variational inference with application to diffusion model acceleration

Reference 49

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This paper cites Treatment effect estimation with disentangled latent factors.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Treatment effect estimation with disentangled latent factors

Reference 50

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A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders

Reference 51

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This paper cites Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation.

A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation

Reference 52

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