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Published in , 1900
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publications
Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making
Published in arXiv preprint arXiv:2309.00805, 2023
Causal machine learning in public policy evaluation–an application to the conditioning of cash transfers in Morocco
Published in arXiv preprint arXiv:2401.07075, 2024
Distilling interpretable causal trees from causal forests
Published in arXiv preprint arXiv:2408.01023, 2024
How do applied researchers use the Causal Forest? A methodological review of a method
Published in arXiv preprint arXiv:2404.13356, 2024
Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective bayesian optimisation
Published in Computational Economics, 2024
This explores the use of interpretable tree models with multi-objective Bayesian optimisation to explore policy eligibility trade-offs.
Transparency challenges in policy evaluation with causal machine learning: improving usability and accountability
Published in Data & Policy, 2024
This discusses issues around transparency when using causal forest models. It covers both the traditional concerns in machine learning around accountability around letting black-box models make decisions that affect people's lives, and also the need for transparency so decision-makers can actually understand the insights in the causal forest model. It argues that given models are largely not making decisions directly, the greater concern is that models are transparent enough the be usable. It discusses some techniques that could be used to address transparency issues.
Randomised trials in Australian public policy: a review
Published in Treasury report, 2025
Understanding the effect of stay-at-home orders on psychological distress during the COVID-19 pandemic: Evidence from a longitudinal study in Australia
Published in PsyArXiv, 2025
How do applied researchers use the causal forest? A methodological review
Published in International Statistical Review, 2025