Studies on Model Inference of Spatial Causal Effects

In an increasingly urbanised world, much of our lives are performed in highly structured space and intertwined with those of others. As such, most of the social phenomenon we are interested in are becoming more difficult to understand and intervene on. Against this background, the urgency in building the capacity to address urban and policy problems has renewed the research interest in spatial causal effects in recent years.

With these challenges, tuning model-based inference to current conceptions of causality is more relevant than ever. The absence of 'space' in classic cause-effect frameworks has put an unfair share of the burden of conveying spatial complexities on written narratives. If our models have so little to say, why use them at all? The proposed study starts from an optimistic note that it is worth encoding space into cause-effect modelling. This optimism will be carefully evaluated and put to practice.

As motivating questions, we ask: What is the role of space in causal process? Where should spatial information appear in cause-effect models and in what form? Conceptually, we question the ambiguities with how space presents as both context to and component of causal processes. This is related to some pressing challenges, such as unobserved confoundedness as a source of model misspecification, and bias due to collinearity in spatial regressions. In response to these challenges, the study will extend the doubly robust (DR) with debiased machine learning (ML) framework to spatial settings. With the new methodological construct, we ask: How does it perform in the presence of spatially structured data? What are the alternatives to make it 'spatial'? Is it as 'robust' in spatial settings as aspatial ones? This studentship aims to answer these questions.