Causal inference is important in medical research to help determine if treatments are beneficial and if natural exposures are harmful. In many settings, data collection makes causal inference ...
Clean energy projects can be cancelled or delayed owing to challenges over the scope of environmental impact assessments (EIAs) and whether they account for direct biophysical impacts and second-order ...
Bayesian networks are probabilistic graphical models that encode conditional dependencies among variables within a directed acyclic graph. In the context of causal inference, these networks provide a ...
Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...
A new study links increased plastic waste imports to measurable increases in PM2.5 near waste disposal sites in Indonesia.
This course is available on the MPhil/PhD in Economic Geography, MPhil/PhD in Environmental Economics, MPhil/PhD in International Relations, MPhil/PhD in Regional and Urban Planning Studies, MRes in ...
Gow, Ian D., David F. Larcker, and Peter C. Reiss. "Causal Inference in Accounting Research." Journal of Accounting Research 54, no. 2 (May 2016): 477–523.
Time series is data collected over time, and statistical learning is a field of statistics and machine learning that develops algorithms to model and interpret this data. Together, they use ...
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