Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors

TL;DR

This paper introduces a novel meta-algorithm for estimating personalized treatment effects by concatenating feature vectors from control and treatment groups, leveraging large control datasets and augmentation techniques to improve estimation accuracy.

Contribution

The paper proposes a new meta-algorithm that uses concatenation and augmentation of feature vectors to estimate conditional treatment effects more accurately than existing methods.

Findings

The proposed algorithm outperforms T-learner and X-learner in simulations.

Augmentation with concatenated feature vectors improves treatment effect estimation.

The method is effective across various outcome function types.

Abstract

A new meta-algorithm for estimating the conditional average treatment effects is proposed in the paper. The main idea underlying the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding examples comprising new feature vectors. The second idea is based on the assumption that the number of controls is rather large and the control outcome function is precisely determined. This assumption allows us to augment treatments by generating feature vectors which are closed to available treatments. The outcome regression function constructed on the augmented set of concatenated feature vectors can be viewed as an estimator of the conditional average treatment effects. A simple modification of the Co-learner based on the random subspace method or the feature bagging is also proposed. Various numerical simulation experiments illustrate the proposed algorithm and show its outperformance in comparison with the well-known T-learner and X-learner for several types of the control and treatment outcome functions.