NDLI: Preventing False Discovery in Interactive Data Analysis Is Hard

Content Provider	IEEE Xplore Digital Library
Author	Hardt, M. Ullman, J.
Copyright Year	2014
Abstract	We show that, under a standard hardness assumption, there is no computationally efficient algorithm that given n samples from an unknown distribution can give valid answers to n3+o(1) adaptively chosen statistical queries. A statistical query asks for the expectation of a predicate over the underlying distribution, and an answer to a statistical query is valid if it is "close" to the correct expectation over the distribution. Our result stands in stark contrast to the well known fact that exponentially many statistical queries can be answered validly and efficiently if the queries are chosen non-adaptively (no query may depend on the answers to previous queries). Moreover, Dwork et al. [1], showed how to accurately answer exponentially many adaptively chosen statistical queries via a computationally inefficient algorithm. They also gave efficient algorithm that can answer nearly n2 adaptively chosen queries, which shows our result is almost quantitatively tight. Conceptually, our result demonstrates that achieving statistical validity alone can be a source of computational intractability in adaptive settings. For example, in the modern large collaborative research environment, data analysts typically choose a particular approach based on previous findings. False discovery occurs if a research finding is supported by the data but not by the underlying distribution. While the study of preventing false discovery in Statistics is decades old, to the best of our knowledge our result is the first to demonstrate a computational barrier. In particular, our result suggests that the perceived difficulty of preventing false discovery in today's collaborative research environment may be inherent.
Sponsorship	IEEE Comput. Soc. Tech. Comm. Math. Found. Comput.
Starting Page	454
Ending Page	463
File Size	355336
Page Count	10
File Format	PDF
ISBN	9781479965175
ISSN	02725428
DOI	10.1109/FOCS.2014.55
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Privacy Encryption Algorithm design and analysis Accuracy Adaptation models Standards Polynomials
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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