Treatment Effect Estimation using Invariant Risk Minimization
This work addresses causal inference challenges in observational studies, offering a method to reduce bias in treatment effect estimation, though it is incremental as it adapts an existing domain generalization framework to a specific problem.
The paper tackles the problem of estimating individual treatment effects from observational data with treatment assignment bias, particularly when there is little support overlap between control and treatment groups, by proposing an invariant risk minimization-based estimator that artificially splits data into domains to improve generalization, showing gains over classical regression approaches in settings with pronounced support mismatch.
Inferring causal individual treatment effect (ITE) from observational data is a challenging problem whose difficulty is exacerbated by the presence of treatment assignment bias. In this work, we propose a new way to estimate the ITE using the domain generalization framework of invariant risk minimization (IRM). IRM uses data from multiple domains, learns predictors that do not exploit spurious domain-dependent factors, and generalizes better to unseen domains. We propose an IRM-based ITE estimator aimed at tackling treatment assignment bias when there is little support overlap between the control group and the treatment group. We accomplish this by creating diversity: given a single dataset, we split the data into multiple domains artificially. These diverse domains are then exploited by IRM to more effectively generalize regression-based models to data regions that lack support overlap. We show gains over classical regression approaches to ITE estimation in settings when support mismatch is more pronounced.