Policymakers combine many different policy tools to achieve emission reductions. However, there remains substantial uncertainty around which mixes of policies are effective. This uncertainty stems from the predominant focus of ex post policy evaluation on isolating effects of single, known...

Outlying observations can bias regression estimates, requiring the use of robust estimators. Comparing robust estimates to those obtained using OLS is a common robustness check, however, such comparisons have been mostly informal due to the lack of available tests. Here we introduce a formal...

Much of empirical research focuses on forward causal questions (``Does X cause Y?'') while answering reverse causal questions (``What causes Y?'') can provide invaluable insights but is difficult to implement in practice. Here we operationalise the modelling of reverse causal questions through...

Quantifying the economic impacts of climate change is crucial to inform mitigation and adaptation policy but faces challenges surrounding impacts of climate extremes and uncertainty around the range of plausible adaptation pathways. We overcome these by using machine learning and econometric...

Extreme weather and conflict may drive forced displacement. However, their individual contribution to displacement is not fully understood due to challenges around isolating individual channels of causality. Here, we use novel disaggregated data on internal displacement in all of Somalia’s...