| EPSRC Reference: |
EP/Y029755/1 |
| Title: |
Bayesian causal estimation via model misspecification |
| Principal Investigator: |
Luo, Dr Y |
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| Department: |
Mathematics |
| Organisation: |
Kings College London |
| Scheme: |
Standard Research - NR1 |
| Starts: |
01 April 2024 |
Ends: |
31 March 2025 |
Value (£): |
61,819
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| EPSRC Research Topic Classifications: |
| Statistics & Appl. Probability |
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| No relevance to Underpinning Sectors |
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Summary on Grant Application Form |
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With an increasing amount of observational data becoming available, there is a pressing need to use appropriate statistical models to answer meaningful causal questions. Causal inference is widely studied across all sciences, such as public transportation and epidemiology. The inference target is a causal estimand which measures the change in the expected outcome of unit under study of different interventions. It has a well-established basis in frequentist semi-parametric theory, with estimation of causal parameters typically conducted via outcome regression and propensity score adjustment. A Bayesian counterpart, however, is not obvious as doubly robust estimation involves a semi-parametric formulation in the absence of a fully specified likelihood function. In this project, we aim to investigate solutions for Bayesian causal inference from standard prior-to-posterior updating, without requiring a parametric family for the observations, and also robust to model misspecfication. We will apply the propose methods to evaluate and quantify London's Stop & Search causal effects on deterring drug offenses in previous decade.
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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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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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