Undersmoothing Causal Estimators with Generative Trees

TL;DR

This paper introduces a generative tree-based method to improve robustness in estimating individualised treatment effects from observational data, especially under covariate shift, outperforming reweighing methods in personalized effect estimation.

Contribution

The paper proposes a novel generative tree approach that directly addresses model misspecification, enhancing the accuracy of individualised treatment effect estimation under covariate shift.

Findings

Generative trees improve robustness in effect estimation.

Reweighing methods struggle with individualised effects.

Proposed method outperforms on individualised treatment effects.

Abstract

Inferring individualised treatment effects from observational data can unlock the potential for targeted interventions. It is, however, hard to infer these effects from observational data. One major problem that can arise is covariate shift where the data (outcome) conditional distribution remains the same but the covariate (input) distribution changes between the training and test set. In an observational data setting, this problem is materialised in control and treated units coming from different distributions. A common solution is to augment learning methods through reweighing schemes (e.g. propensity scores). These are needed due to model misspecification, but might hurt performance in the individual case. In this paper, we explore a novel generative tree based approach that tackles model misspecification directly, helping downstream estimators achieve better robustness. We show empirically that the choice of model class can indeed significantly affect the final performance and that reweighing methods can struggle in individualised effect estimation. Our proposed approach is competitive with reweighing methods on average treatment effects while performing significantly better on individualised treatment effects.