Improving massive experiments with threshold blocking

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Higgins, Michael J.Kansas State Univ, Dept Stat, Manhattan, KS 66506 USA (författare)

Improving massive experiments with threshold blocking

Artikel/kapitelEngelska2016

Förlag, utgivningsår, omfång ...

2016-07-05
Proceedings of the National Academy of Sciences,2016
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LIBRIS-ID:oai:DiVA.org:uu-299820
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-299820URI
https://doi.org/10.1073/pnas.1510504113DOI

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Språk:engelska
Sammanfattning på:engelska

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Ämneskategori:ref swepub-contenttype
Ämneskategori:art swepub-publicationtype

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Inferences from randomized experiments can be improved by blocking: assigning treatment in fixed proportions within groups of similar units. However, the use of the method is limited by the difficulty in deriving these groups. Current blocking methods are restricted to special cases or run in exponential time; are not sensitive to clustering of data points; and are often heuristic, providing an unsatisfactory solution in many common instances. We present an algorithm that implements a widely applicable class of blocking-threshold blocking-that solves these problems. Given a minimum required group size and a distance metric, we study the blocking problem of minimizing the maximum distance between any two units within the same group. We prove this is a nondeterministic polynomial-time hard problem and derive an approximation algorithm that yields a blocking where the maximum distance is guaranteed to be, at most, four times the optimal value. This algorithm runs in O(n log n) time with O(n) space complexity. This makes it, to our knowledge, the first blocking method with an ensured level of performance that works in massive experiments. Whereas many commonly used algorithms form pairs of units, our algorithm constructs the groups flexibly for any chosen minimum size. This facilitates complex experiments with several treatment arms and clustered data. A simulation study demonstrates the efficiency and efficacy of the algorithm; tens of millions of units can be blocked using a desktop computer in a few minutes.

Ämnesord och genrebeteckningar

SAMHÄLLSVETENSKAP Ekonomi och näringsliv Nationalekonomi hsv//swe
SOCIAL SCIENCES Economics and Business Economics hsv//eng
experimental design; blocking; big data; causal inference
Nationalekonomi
Economics

Biuppslag (personer, institutioner, konferenser, titlar ...)

Sävje, FredrikUppsala universitet,Nationalekonomiska institutionen(Swepub:uu)fresa529 (författare)
Sekhon, Jasjeet S.Univ Calif Berkeley, Dept Polit Sci, Berkeley, CA 94720 USA;Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA (författare)
Kansas State Univ, Dept Stat, Manhattan, KS 66506 USANationalekonomiska institutionen (creator_code:org_t)

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Ingår i:Proceedings of the National Academy of Sciences of the United States of America: Proceedings of the National Academy of Sciences13:27, s. 7369-73760027-84241091-6490

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Av författaren/redakt...: Higgins, Michael ...; Sävje, Fredrik; Sekhon, Jasjeet ...

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SAMHÄLLSVETENSKAP: SAMHÄLLSVETENSKA ...; och Ekonomi och näri ...; och Nationalekonomi

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