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Communication Dans Un Congrès Année : 2021

Individual Treatment Prescription Effect Estimation in a Low Compliance Setting

Thibaud Rahier
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Amélie Héliou
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Matthieu Martin
Christophe Renaudin
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Eustache Diemert
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Résumé

Individual Treatment Effect (ITE) estimation is an extensively researched problem, with applications in various domains. We model the case where there exists heterogeneous non-compliance to a randomly assigned treatment, a typical situation in health (because of non-compliance to prescription) or digital advertising (because of competition and ad blockers for instance). The lower the compliance, the more the effect of treatment prescription − or individual prescription effect (IPE) − signal fades away and becomes harder to estimate. We propose a new approach for the estimation of the IPE that takes advantage of observed compliance information to prevent signal fading. Using the Structural Causal Model framework and do-calculus, we define a general mediated causal effect setting and propose a corresponding estimator which consistently recovers the IPE with asymptotic variance guarantees. Finally, we conduct experiments on both synthetic and real-world datasets that highlight the benefit of the approach, which consistently improves state-of-the-art in low compliance settings. CCS CONCEPTS • Mathematics of computing → Probability and statistics; Causal networks; • Information systems → Online advertising.
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Dates et versions

hal-03339108 , version 1 (14-09-2021)

Identifiants

Citer

Thibaud Rahier, Amélie Héliou, Matthieu Martin, Christophe Renaudin, Eustache Diemert. Individual Treatment Prescription Effect Estimation in a Low Compliance Setting. KDD 2021, Aug 2021, Singapore (virtual), Singapore. pp.1399-1409, ⟨10.1145/3447548.3467343⟩. ⟨hal-03339108⟩
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