Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors
This work addresses the challenge of causal inference in personalized medicine or policy evaluation, but it is incremental as it builds on existing meta-learner frameworks.
The authors tackled the problem of estimating personalized heterogeneous treatment effects by proposing a meta-algorithm that concatenates and augments feature vectors from control and treatment groups, resulting in improved performance over T-learner and X-learner in numerical simulations.
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.