Removing Hidden Confounding by Experimental Grounding

TL;DR

This paper presents a novel method that leverages limited experimental data to correct hidden confounding in observational causal models, improving causal inference accuracy with weaker assumptions.

Contribution

The authors introduce a new approach that uses limited experimental data to adjust for hidden confounding in large observational datasets, with proven consistency under weaker assumptions.

Findings

Effective correction of hidden confounding demonstrated on real-world educational data.

Method outperforms existing approaches in scenarios with limited experimental data.

Provides theoretical guarantees for estimator consistency.

Abstract

Observational data is increasingly used as a means for making individual-level causal predictions and intervention recommendations. The foremost challenge of causal inference from observational data is hidden confounding, whose presence cannot be tested in data and can invalidate any causal conclusion. Experimental data does not suffer from confounding but is usually limited in both scope and scale. We introduce a novel method of using limited experimental data to correct the hidden confounding in causal effect models trained on larger observational data, even if the observational data does not fully overlap with the experimental data. Our method makes strictly weaker assumptions than existing approaches, and we prove conditions under which it yields a consistent estimator. We demonstrate our method's efficacy using real-world data from a large educational experiment.