Reproduce “Learning Treatment Representations for Downstream Instrumental Variable Regression” on OpenResearch

About this paper

Shiangyi Lin, Hui Lan, Vasilis Syrgkanis

Researchers at Stanford University introduced Instrument-Guided Representation Learning (IGRL), comprising linear (LIRR) and non-linear (IRAE) variants, which integrates instrumental variables directly into the process of learning low-dimensional treatment representations. This approach mitigates omitted variable bias prevalent in two-step dimension reduction, yielding improved causal intervention outcomes on synthetic and MNIST datasets, where IRAE achieved up to 6.5253 "-+ 6.0132 improvement in non-linear settings.

Key insights

• Unsupervised dimension reduction without considering instruments introduces omitted variable bias, invalidating causal inference from downstream IV analysis.
• Integrating instrumental variables directly into representation learning is crucial for constructing low-dimensional treatment representations that satisfy IV assumptions.
• The learned instrument-guided representations enable the identification of outcome-improving intervention directions that are causally valid, with theoretical guarantees for both linear and non-linear settings.

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