Title: Artificial intelligence for causality with applications in the social and economic sciences
Funding: Home/EU/International fees and a stipend of £14,777 per year (including an AQM enhancement of £3,250 for each PhD year)
Application deadline: 17 February 2020
Start date: 1 October 2020
We seek a talented student with a background in computer science, econometrics or statistics. This studentship will be jointly supervised by Professor Clarke and Dr Spyros Samothrakis, and will address the following research areas:
(i) Causal discovery for high-dimensional feature spaces: Most classic causal discovery methods take the covariate/treatment to be a high-level feature that can be manipulated by direct intervention or policy change. But some data sets only contain information on such low-level features - forming high-dimensional feature spaces - that the idea of manipulating these features is meaningless (e.g. a pixel in a video stream). The aim of this project is to use the latest techniques to identify manipulable high-level features from rich data on low-level features in order to carry out causal analysis using Artificial Intelligence techniques (e.g. reinforcement learning, causal inference) (Bengio et al. 2019).
(ii) Embedding common knowledge in causal effect identification: To understand the effect of a treatment without resorting to direct experiments requires us to incorporate knowledge about the social process; this should in turn allow us to learn a model for the interventional distribution from observational data. In this project, a body of common knowledge related to a topic from, e.g., sociology or economics, will be used to guide the creation of an interventional model using a variety of approaches, e.g. simulating scenarios to integrate with observed data or Bayesian methods.
(iii) Speeding up experimental designs by incorporating observational data: If available, abundant observational data can be used to help speed up the experimental process (e.g. Zhang et.al. 2017, Dudik et.al. 2014). The purpose of this project is to use advances in machine learning, causal inference and logic to identify from observational the further experimental interventions or data collection we need to carry out in order to make inferences about the causal effects of treatments.