| EPSRC Reference: |
EP/N020294/1 |
| Title: |
Causal Inference from Partial Statistical Information |
| Principal Investigator: |
Evans, Professor R J |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Statistics |
| Organisation: |
University of Oxford |
| Scheme: |
First Grant - Revised 2009 |
| Starts: |
01 April 2016 |
Ends: |
03 July 2018 |
Value (£): |
99,005
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| EPSRC Research Topic Classifications: |
| Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
Much of statistical theory is concerned with the analysis of a single
data set, obtained from an experiment, survey, or study. However in
many fields which most urgently demand new statistical methodology,
problems do not fit this paradigm. Instead data are gathered from
multiple settings under different experimental conditions, which may
measure different variables or be sampling from different
populations. This leads to situations in which it is unclear how to
combine statistical information in a way that provides a coherent
solution agreeing with all studies, and which properly quantifies the
uncertainty in the estimation process.
The gold-standard method for answering causal questions is the
randomised controlled trial (RCT), but RCTs are extremely expensive
and usually end without a positive result. Meanwhile biology and
medicine are at the forefront of the big data revolution, as more and
more is being measured at greater and greater resolutions; the 100,000
Genomes Project and UK Biobank each contain tens of thousands of
genetic and phenotypic measurements on hundreds of thousands of
people. Electronic healthcare records will generate Terabytes of
medical information about tens of millions of people. Many of the
quantities measured in these data sets cannot be experimentally
controlled for practical, financial or ethical reasons.
This project aims to uncover how much we can learn about the causal
mechanisms underlying multiple large and complex data sets without
performing experiments, or with limited experimental data: to learn
as much as possible about the world just by looking.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.ox.ac.uk |