mlsanityData to 7 Oct 2026

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

arXiv 2207.09139 ↗First posted 19 Jul 2022cs.LG stat.MLcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 44 of its 96 references that are in the map. First posted on arXiv 19 Jul 2022.

Builds on

44 of 96 references.

References · 44

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32 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2.

4 papers.

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  4. 2025Towards a Relationship-Aware Transformer for Tabular Datacited by 0