Untangling Effect and Side Effect: Consistent Causal Inference in Non-Targeted Trials

Georgios Mavroudeas, Malik Magdon-Ismail, Kristin Bennett, Jason Kuruzovich

Keywords: Causal Inference, Non Targeted Trials, Machine Learning, Heterogeneous Treatment Effects

TL;DR: We propose an algorithm that provably recovers hidden effect groups in causal studies

Abstract:

A treatment is usually appropriate for some group (the ``sick" group) on whom it has an effect, but it can also have a side-effect when given to subjects from another group (the ``healthy" group). In a non-targeted trial both sick and healthy subjects may be treated, producing
heterogeneous effects within the treated group. Inferring the correct treatment effect on the sick population is then
difficult, because the effect and side-effect are tangled. We propose an efficient nonparametric approach to untangling the effect and side-effect, called PCM (pre-cluster and merge). We prove its asymptotic consistency in a general setting and
show, on synthetic data,
more than a 10x improvement in accuracy over existing state-of-the-art.

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