Treatment Effect Estimation using Invariant Risk Minimization

• URL: http://arxiv.org/abs/2103.07788v1
• Date: Sat, 13 Mar 2021 20:42:04 GMT
• Title: Treatment Effect Estimation using Invariant Risk Minimization
• Authors: Abhin Shah, Kartik Ahuja, Karthikeyan Shanmugam, Dennis Wei, Kush Varshney, Amit Dhurandhar
• Abstract summary: In this work, we propose a new way to estimate the causal individual treatment effect (ITE) using the domain generalization framework of invariant risk minimization (IRM) 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 show gains over classical regression approaches to ITE estimation in settings when support mismatch is more pronounced.
• Score: 32.9769365726994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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