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.
This paper discusses experimental design to estimate welfare-maximizing policies . 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 construct a test for whether a welfare-improving treatment configuration exists and hence worth learning by conducting a larger scale experiment. Second, we introduce an adaptive randomization procedure to estimate welfare-maximizing individual treatment allocation rules valid under unobserved interference. We derive asymptotic properties of the marginal effects estimators and finite-sample regret guarantees of the policy . Finally, we illustrate the method's advantage in simulations calibrated to an existing experiment on information diffusion.
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for inference and estimation of individualized treatment allocation rules in the presence of unknown interference
to estimate welfare-maximizing policies
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design a short pilot study with few clusters to test whether there exists
construct a test for whether
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exists
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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.
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a sequential
an adaptive
D
(and partial)
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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
derive asymptotic properties of the marginal effects estimators and finite-sample
D
estimated through the sequential experiment