Graph Intervention Networks for Causal Effect Estimation

• URL: http://arxiv.org/abs/2106.01939v1
• Date: Thu, 3 Jun 2021 15:41:00 GMT
• Title: Graph Intervention Networks for Causal Effect Estimation
• Authors: Jean Kaddour, Qi Liu, Yuchen Zhu, Matt J. Kusner, Ricardo Silva
• Abstract summary: We address the estimation of conditional average treatment effects (CATEs) when treatments are graph-structured. We propose a plug-in estimator that decomposes CATE estimation into separate, simpler optimization problems.
• Score: 30.516184324213874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the estimation of conditional average treatment effects (CATEs) when treatments are graph-structured (e.g., molecular graphs of drugs). Given a weak condition on the effect, we propose a plug-in estimator that decomposes CATE estimation into separate, simpler optimization problems. Our estimator (a) isolates the causal estimands (reducing regularization bias), and (b) allows one to plug in arbitrary models for learning. In experiments with small-world and molecular graphs, we show that our approach outperforms prior approaches and is robust to varying selection biases. Our implementation is online.

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