Affiliation:
1. Institute of Computer Science and Technology, Peter the Great St. Petersburg Polytechnic University, Polytechnicheskaya, 29, 195251 St. Petersburg, Russia
Abstract
A new method for estimating the conditional average treatment effect is proposed in this 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 and the number of treatments is small. TNW-CATE uses the Nadaraya–Watson regression for predicting outcomes of patients from 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 transfer learning when domains of source and target data are similar, but the 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 control and treatment outcome functions. The code of proposed algorithms implementing TNW-CATE is publicly available.
Funder
Russian Science Foundation
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
Reference99 articles.
1. Lu, M., Sadiq, S., Feaster, D., and Ishwaran, H. (2017). Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods. arXiv.
2. Shalit, U., Johansson, F., and Sontag, D. (2017, January 6–11). Estimating individual treatment effect: Generalization bounds and algorithms. Proceedings of the 34th International Conference on Machine Learning (ICML 2017), Sydney, Australia.
3. Estimating Heterogeneous Treatment Effects with Observational Data;Xie;Sociol. Methodol.,2012
4. Estimating Individual Treatment Effects using Non-Parametric Regression Models: A Review;Caron;J. R. Stat. Soc. Ser. A Stat. Soc.,2022
5. Heterogeneous Treatment Effects in the Presence of Self-Selection: A Propensity Score Perspective;Zhou;Sociol. Methodol.,2020
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