Economist · Data Scientist · Oxford
I'm Isaac, an economist working at the intersection of causal inference, machine learning, and the study of belief. I'm currently completing an MSc in Social Data Science at the University of Oxford and working as a predoctoral research fellow.
My path runs from philosophy and ethics through First Class Honours in economics to applied ML — which is another way of saying I care as much about whether a question is well-posed as whether the standard errors are clustered correctly.
Does telling people what economists actually agree on change what they believe about economic policy? I'm designing and running an online randomised controlled trial to estimate the causal effect of expert-consensus messaging on policy beliefs — adapting a methodology from public-health work on vaccine hesitancy to an economic setting. Supervised by Prof. Greg Taylor (Oxford Internet Institute).
Fielded via Qualtrics and Prolific, with an analysis pipeline in R and Python covering treatment-effect estimation, multiple-hypothesis adjustment, and balance and attrition diagnostics.
Why did American economic sentiment stay grim while the fundamentals recovered? I replicated the most prominent academic explanation using a conceptually identical procedure — and found that the headline result failed to replicate. I then developed a partisan-asymmetry explanation that accounted for a larger share of the sentiment–reality gap than any competing account in the literature at the time of writing. Supervised by Prof. Richard Holden (UNSW).
Along the way I built a novel Google Trends–based real-time sentiment index that mechanically rules out salient forms of partisan signalling.
An economic-history paper used HSV colour histograms from European oil paintings to build high-frequency historical indices of economic growth. I replicated its core methodology (including PCA-based feature extraction) from the written description alone, then extended it with features from a pre-trained neural network to test whether composition, texture, and style encode growth signal beyond colour.
Working on the mathematics of database theory — dependency theory, conjunctive queries, expressive power, finite model theory — and applying it to theoretical models of cognitive reasoning for a working paper. I lead weekly presentations on these topics to a team of economics professors and conduct literature reviews across the neuroeconomics literature.
Building a real-time, end-to-end Australian economic sentiment index from Google Trends data — scraping, cleaning, and time-series construction in R, Python, and Stata, including forking and modifying an open-source R package to adapt its sampling behaviour to the project's needs.
Private academic tutor for high-school students, managing my own client roster; and ward assistant on the casual roster at RPA Hospital, working across the COVID-19 clinic, emergency department, and ICU.
Applied statistics, machine learning, data science, research design. Thesis under Prof. Greg Taylor.
PhD-level coursework in econometrics, causal inference, micro, macro, and health economics.
Econometrics and empirical methods alongside applied ethics, political economy, and international relations.
Award for Academic Excellence and Scholars Award; distinguished achiever.
Essays coming soon.