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Creators/Authors contains: "Orlandi, Vittorio"

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  1. ; ; ; ; (, Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI))
    null (Ed.)
    We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space. These regions are large enough that many matches are created for each unit and small enough that the treatment effect is roughly constant throughout. The regions are found as either the solution to a mixed integer program, or using a (fast) approximation algorithm. The result is an interpretable and tailored estimate of the causal effect for each unit. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; (, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics)
    null (Ed.)
    We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others’ outcomes. Traditional treatment effect estimators for randomized experiments are biased and error prone in this setting. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs. The matches that we construct are interpretable and high-quality. Our method can be extended easily to accommodate additional unit-level covariate information. We show empirically that our method performs better than other existing methodologies for this problem, while producing meaningful, interpretable results. 
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    Accepted Manuscript Full Text Available