Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE | Paper Citation Record

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This paper cites Estimating the labor market impact of voluntary military service using social security data on military applicants, 1995.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Estimating the labor market impact of voluntary military service using social security data on military applicants, 1995

Reference 1

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This paper cites Estimating the effect of training programs on earnings.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Estimating the effect of training programs on earnings

Reference 2

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This paper cites Learning representations by maximizing mutual information across views.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Learning representations by maximizing mutual information across views

Reference 3

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This paper cites Controlling selection bias in causal inference.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Controlling selection bias in causal inference

Reference 4

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This paper cites Recovering causal effects from selection bias.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Recovering causal effects from selection bias

Reference 5

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This paper cites D o C o G en: D omain counterfactual generation for low resource domain adaptation.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE D o C o G en: D omain counterfactual generation for low resource domain adaptation

Reference 6

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This paper cites Adversarial de-confounding in individualised treatment effects estimation.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Adversarial de-confounding in individualised treatment effects estimation

Reference 7

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This paper cites DISCO : Distilling counterfactuals with large language models.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE DISCO : Distilling counterfactuals with large language models

Reference 8

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This paper cites Avoiding post-treatment bias in audit experiments.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Avoiding post-treatment bias in audit experiments

Reference 9

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This paper cites Generalized adjustment under confounding and selection biases.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Generalized adjustment under confounding and selection biases

Reference 10

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This paper cites On inductive biases for heterogeneous treatment effect estimation.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE On inductive biases for heterogeneous treatment effect estimation

Reference 11

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This paper cites In search of insights, not magic bullets: Towards demystification of the model selection dilemma in heterogeneous treatment effect estimation.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE In search of insights, not magic bullets: Towards demystification of the model selection dilemma in heterogeneous treatment effect estimation

Reference 12

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Observation a7a5cf3f-e8a1-4f1b-a4b4-013e729da8f1 · outbound

This paper cites Really doing great at estimating cate? a critical look at ml benchmarking practices in treatment effect estimation.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Really doing great at estimating cate? a critical look at ml benchmarking practices in treatment effect estimation

Reference 13

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This paper cites Meal simulation model of the glucose-insulin system.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Meal simulation model of the glucose-insulin system

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.929190Z

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This paper cites Counterfactual mri generation with denoising diffusion models for interpretable alzheimer's disease effect detection.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Counterfactual mri generation with denoising diffusion models for interpretable alzheimer's disease effect detection

Reference 15

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This paper cites Density estimation using Real NVP.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Density estimation using Real NVP

Reference 16

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This paper cites CORE : A retrieve-then-edit framework for counterfactual data generation.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE CORE : A retrieve-then-edit framework for counterfactual data generation

Reference 17

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This paper cites Minimax optimal nonparametric estimation of heterogeneous treatment e\ ffects.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Minimax optimal nonparametric estimation of heterogeneous treatment e\ ffects

Reference 18

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This paper cites On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset

Reference 19

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This paper cites Medjourney: Counterfactual medical image generation by instruction-learning from multimodal patient journeys.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Medjourney: Counterfactual medical image generation by instruction-learning from multimodal patient journeys

Reference 20

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This paper cites Counterfactual regression with importance sampling weights.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Counterfactual regression with importance sampling weights

Reference 21

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This paper cites Learning disentangled representations for counterfactual regression.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Learning disentangled representations for counterfactual regression

Reference 22

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This paper cites PEREIRA, and MARGIT TAVITS.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE PEREIRA, and MARGIT TAVITS

Reference 23

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This paper cites Extracting post-treatment covariates for heterogeneous treatment effect estimation.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Extracting post-treatment covariates for heterogeneous treatment effect estimation

Reference 24

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This paper cites Causal inference without balance checking: Coarsened exact matching.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Causal inference without balance checking: Coarsened exact matching

Reference 25

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This paper cites Diffusion models for counterfactual explanations.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Diffusion models for counterfactual explanations

Reference 26

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This paper cites Learning causal effects via weighted empirical risk minimization.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Learning causal effects via weighted empirical risk minimization

Reference 27

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This paper cites Deepmatch: Balancing deep covariate representations for causal inference using adversarial training.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Deepmatch: Balancing deep covariate representations for causal inference using adversarial training

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.810414Z

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This paper cites A survey on simulators for testing self-driving cars.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE A survey on simulators for testing self-driving cars

Reference 29

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This paper cites Towards optimal doubly robust estimation of heterogeneous causal effects.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Towards optimal doubly robust estimation of heterogeneous causal effects

Reference 30

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This paper cites A hard unsolved problem? post-treatment bias in big social science questions.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE A hard unsolved problem? post-treatment bias in big social science questions

Reference 31

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This paper cites o ren R K \.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE o ren R K \

Reference 32

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This paper cites Explaining counterfactual images.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Explaining counterfactual images

Reference 33

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This paper cites Challenging common assumptions in the unsupervised learning of disentangled representations.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Challenging common assumptions in the unsupervised learning of disentangled representations

Reference 34

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This paper cites a tsch, Bernhard Sch \.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE a tsch, Bernhard Sch \

Reference 35

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This paper cites Generate your counterfactuals: Towards controlled counterfactual generation for text.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Generate your counterfactuals: Towards controlled counterfactual generation for text

Reference 36

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This paper cites Learning recourse on instance environment to enhance prediction accuracy.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Learning recourse on instance environment to enhance prediction accuracy

Reference 37

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This paper cites Continuous treatment effect estimation using gradient interpolation and kernel smoothing.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Continuous treatment effect estimation using gradient interpolation and kernel smoothing

Reference 38

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This paper cites Pairnet: Training with observed pairs to estimate individual treatment effect.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Pairnet: Training with observed pairs to estimate individual treatment effect

Reference 39

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This paper cites VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments

Reference 40

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Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Quasi-oracle estimation of heterogeneous treatment effects

Reference 41

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This paper cites Adversarial Balancing for Causal Inference.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Adversarial Balancing for Causal Inference

Reference 42

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This paper cites Counterfactual Image Editing.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Counterfactual Image Editing

Reference 43

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This paper cites Deep structural causal models for tractable counterfactual inference.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Deep structural causal models for tractable counterfactual inference

Reference 44

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Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Pearl and Cambridge University Press

Reference 45

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This paper cites Conditioning on post-treatment variables.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Conditioning on post-treatment variables

Reference 46

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Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Unresolved cited work

Reference 47

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This paper cites Generating realistic natural language counterfactuals.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Generating realistic natural language counterfactuals

Reference 48

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This paper cites Estimation of regression coefficients when some regressors are not always observed.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Estimation of regression coefficients when some regressors are not always observed

Reference 49

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This paper cites The central role of the propensity score in observational studies for causal effects.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE The central role of the propensity score in observational studies for causal effects

Reference 50

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This paper cites Counterfactual generative networks.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Counterfactual generative networks

Reference 51

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Observation 75716458-25ba-40c5-b9a6-165e20b2a34f · outbound

This paper cites Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks

Reference 52

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Observation 91cc679b-e6f2-497e-8080-acb76ebe681a · outbound

This paper cites Learning counterfactual representations for estimating individual dose-response curves.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Learning counterfactual representations for estimating individual dose-response curves

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.642322Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 44a55ed5-6529-47dd-b7ad-f1b2344191e3 · outbound

This paper cites Estimating individual treatment effect: generalization bounds and algorithms.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Estimating individual treatment effect: generalization bounds and algorithms

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T20:39:47.275892Z

Source-reported events for the cited work

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Observation 6ac0224d-e4a1-4ffe-99ce-02862708b5d9 · outbound

This paper cites Estimating individual treatment effect: generalization bounds and algorithms.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Estimating individual treatment effect: generalization bounds and algorithms

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.629830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 199ae42d-8f93-4198-be8e-119b9093d373 · outbound

This paper cites Adapting neural networks for the estimation of treatment effects.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Adapting neural networks for the estimation of treatment effects

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-08T20:39:47.282494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d993b95-a382-4f40-a4f3-123b69c42ba6 · outbound

This paper cites Matching methods for causal inference: A review and a look forward.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Matching methods for causal inference: A review and a look forward

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.609586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 05019ef4-3730-4d00-afb8-b93e16d4c6bc · outbound

This paper cites Designing counterfactual generators using deep model inversion.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Designing counterfactual generators using deep model inversion

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.597166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 640e1507-b5b9-42c4-955d-4af2c8bb1547 · outbound

This paper cites u gelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Sch \.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE u gelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Sch \

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.586486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 91c72b4d-82f2-41a1-8d69-b5b2b061ab3c · outbound

This paper cites Wang, Natalia S.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Wang, Natalia S

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.575745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 692ebc73-ecb6-4a8b-9aa3-16b76a55a884 · outbound

This paper cites Optimal transport for treatment effect estimation.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Optimal transport for treatment effect estimation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.565344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e9aa8f62-c67d-4eed-8f3e-f43164e01584 · outbound

This paper cites Stable estimation of heterogeneous treatment effects.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Stable estimation of heterogeneous treatment effects

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.553253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 977251cc-3dd6-47e3-a876-a7ac0cb0734e · outbound

This paper cites Simglucose v0.2.1 (2018) [Online] , 2018.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Simglucose v0.2.1 (2018) [Online] , 2018

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.538524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fa20de00-c814-4317-87a1-c4673d65d7ca · outbound

This paper cites Representation learning for treatment effect estimation from observational data.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Representation learning for treatment effect estimation from observational data

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.526489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e0b0ecc9-b090-48c2-a1bf-e8b12eca7fc6 · outbound

This paper cites Ganite: Estimation of individualized treatment effects using generative adversarial nets.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Ganite: Estimation of individualized treatment effects using generative adversarial nets

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.514766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 971fe9b6-a84e-4af2-bdcc-7be09a077d7d · outbound

This paper cites Learning overlapping representations for the estimation of individualized treatment effects.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Learning overlapping representations for the estimation of individualized treatment effects

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.502440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 60551f86-aa81-4bf4-9625-9592bea4b6ab · outbound

This paper cites Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-08T20:39:47.317433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5ea8d00-07e3-4832-9531-d9dcb33dba10 · outbound

This paper cites Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T20:39:47.491020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9a0424cf-05a5-4167-857f-224eb30c5aa7 · outbound

This paper cites write newline.

Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE write newline

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T20:39:47.324031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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