Undersmoothing Causal Estimators with Generative Trees
This addresses the challenge of personalized treatment effect estimation in fields like healthcare or policy, offering a more robust alternative to existing methods, though it appears incremental as it builds on generative models and causal inference techniques.
The paper tackles the problem of inferring individualized treatment effects from observational data, where covariate shift and model misspecification hinder performance, by proposing a generative tree-based approach that improves robustness and outperforms reweighing methods on individualized effects.
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.