This paper discusses experimental design for inference and estimation of individualized treatment allocation rules in the presence of unknown interference , with units URLanized into large independent clusters . The contribution  is two-fold. First, we design a short pilot study with few clusters for testing whether base-line interventions are welfare-maximizing, with its rejection motivating larger-scale experimentation . Second, we introduce an adaptive randomization procedure to estimate welfare-maximizing individual treatment allocation rules valid under unobserved  interference. We propose non-parametric estimators of direct treatments and marginal spillover effects, which serve for hypothesis testing and policy-design. We discuss the asymptotic properties of the estimators and small sample regret guarantees of the estimated policy . Finally, we illustrate the method's advantage in simulations calibrated to an existing experiment on information diffusion.
This paper discusses experimental design for inference and estimation of individualized treatment allocation rules in the presence of unknown interference . We consider a setting where units URLanized into large , finitely many independent clusters and interact over unobserved dimensions within each cluster . The contribution of this paper is two-fold. First, we design a short pilot study with few clusters to test whether there exists a welfare-improving treatment configuration and hence worth learning by conducting a larger scale experiment. We propose a practical test that uses information on the marginal effect of the policy on welfare to compare the base-line intervention against any possible alternative . Second, we introduce a sequential randomization procedure to estimate welfare-maximizing individual treatment allocation rules valid under unobserved (and partial) interference. We propose non-parametric estimators of direct treatments and marginal spillover effects, which serve for hypothesis testing and policy-design. We derive the estimators' asymptotic properties, and small sample regret guarantees of the policy estimated through the sequential experiment . Finally, we illustrate the method's advantage in simulations calibrated to an existing experiment on information diffusion.
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, with
. We consider a setting where
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independent clusters
, finitely many independent clusters and interact over unobserved dimensions within each cluster
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of this paper
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for testing whether
to test whether there exists a welfare-improving treatment configuration and hence worth learning by conducting a larger scale experiment. We propose a practical test that uses information on the marginal effect of the policy on welfare to compare the
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interventions are welfare-maximizing, with its rejection motivating larger-scale experimentation
intervention against any possible alternative
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an adaptive
a sequential
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(and partial)
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discuss the asymptotic properties of the estimators
derive the estimators' asymptotic properties,
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estimated policy
policy estimated through the sequential experiment