CETransformer: Casual Effect Estimation via Transformer Based Representation Learning

Guo, Zhenyu; Zheng, Shuai; Liu, Zhizhe; Yan, Kun; Zhu, Zhenfeng

doi:10.1007/978-3-030-88013-2_43

Cited by 10 publications

(20 citation statements)

References 17 publications

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“…In many works, representation distribution balancing is combined with other domain ideas. An ABCEI [122] combines a GAN [112] with a mutual information estimator regularization structure to balance the covariate distributions of the treatment and control groups in the representation space; CETransformer [127] creates a balanced covariate representation using the attention mechanism; As TransTEE [76] extends the representation distribution balance method to Continuous, Structured, and Dose-Related treatments, it makes causal effect estimation a more open-ended problem; SCI [75] introduces the concept of a subspace as shown in Figure 4, integrating the covariates into a common subspace, a treatment subspace, and a control subspace simultaneously, thereby obtaining a balanced representation and two specific representations. Afterwards, the public representation is connected to the specific representation of the treatment group and of the control group, and two potential results are obtained from the reconstruction and prediction network.…”

Section: Representation Of Distribution Balance Methodsmentioning

confidence: 99%

“…The VCNet [58] model, implements an estimator of continuous mean dose-response curves. As of May 2021, NCoRE [126] uses cross-treatment interaction modeling to understand the underlying causal processes that produce multiple treatment combinations.After that, CETransformer [127] uses Transformer [128] to characterize covariates, and the attention mechanism is focused on the correlation among covariates. Following that, DONUT [129] and DeR-CFR [70] optimize based on previous work.…”

Section: Overviewmentioning

confidence: 99%

“…By October 2020, ABCEI [122] balances latent representation spatial covariate distribution by using GAN networks. Besides, CETransformer [127] combines attention mechanisms with GAN networks in July 2021 to learn a balanced covariate representation.…”

Section: Classificationmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Deep Causal Model

Li¹,

Zhu²

2022

Preprint

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The concept of causality plays an important role in human cognition . In the past few decades, causal inference has been well developed in many fields, such as computer science, medicine, economics, and education. With the advancement of deep learning techniques, it has been increasingly used in causal inference against counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective optimization functions to estimate counterfactual data unbiasedly based on the different optimization methods. This paper focuses on the survey of the deep causal models, and its core contributions are as follows: 1) we provide relevant metrics under multiple treatments and continuous-dose treatment; 2) we incorporate a comprehensive overview of deep causal models from both temporal development and method classification perspectives; 3) we assist a detailed and comprehensive classification and analysis of relevant datasets and source code.

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Section: Representation Of Distribution Balance Methodsmentioning

confidence: 99%

Section: Overviewmentioning

confidence: 99%

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A Survey of Deep Causal Model

Li¹,

Zhu²

2022

Preprint

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“…Transformer-based architectures using attention operations [68] were also applied to solving the CATE estimating problem [29,30,31,32]. Ideas of applying the transfer learning technique to the CATE estimation were considered in [69,70,71,8].…”

Section: Related Workmentioning

confidence: 99%

“…This is only a small part of all publications which are devoted to solving the problem of estimating CATE. Various approaches were used for solving the problem, including the support vector machine [25], tree-based models [9], neural networks [26,27,8,28], transformers [29,30,31,32].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression

Konstantinov¹,

R.²,

Utkin³

2022

Preprint

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A new method for estimating the conditional average treatment effect is proposed in the paper. It is called TNW-CATE (the Trainable Nadaraya-Watson regression for CATE) and based on the assumption that the number of controls is rather large whereas the number of treatments is small. TNW-CATE uses the Nadaraya-Watson regression for predicting outcomes of patients from the control and treatment groups. The main idea behind TNW-CATE is to train kernels of the Nadaraya-Watson regression by using a weight sharing neural network of a specific form. The network is trained on controls, and it replaces standard kernels with a set of neural subnetworks with shared parameters such that every subnetwork implements the trainable kernel, but the whole network implements the Nadaraya-Watson estimator. The network memorizes how the feature vectors are located in the feature space. The proposed approach is similar to the transfer learning when domains of source and target data are similar, but tasks are different. Various numerical simulation experiments illustrate TNW-CATE and compare it with the well-known T-learner, S-learner and X-learner for several types of the control and treatment outcome functions. The code of proposed algorithms implementing TNW-CATE is available in https://github.com/Stasychbr/TNW-CATE.

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A survey of deep causal models and their industrial applications

Li,

Guo,

Qiang

2024

Artif Intell Rev

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The notion of causality assumes a paramount position within the realm of human cognition. Over the past few decades, there has been significant advancement in the domain of causal effect estimation across various disciplines, including but not limited to computer science, medicine, economics, and industrial applications. Given the continous advancements in deep learning methodologies, there has been a notable surge in its utilization for the estimation of causal effects using counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective functions to estimate counterfactual data unbiasedly. Different from the existing surveys on causal models in machine learning, this review mainly focuses on the overview of the deep causal models based on neural networks, and its core contributions are as follows: (1) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; (2) we outline some typical applications of causal effect estimation to industry; (3) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments.

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CETransformer: Casual Effect Estimation via Transformer Based Representation Learning

Cited by 10 publications

References 17 publications

A Survey of Deep Causal Model

A Survey of Deep Causal Model

Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression

A survey of deep causal models and their industrial applications

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