Establishing cause and effect relationships outside of expe rimental settings is well known to be a challenging task with far reaching implications for the scientific enterprise across research disciplines. N onetheless\, due to ethical concerns or practical considerations\, experim ental evaluation of causal effects is often not feasible. For example\, le arning about the potential harmful effect of air pollution on asthma attac ks cannot readily be studied in a randomized experiment for the obvious et hical reasons\; likewise evaluating the real-world effectiveness and safet y of COVID-19 vaccines necessarily requires working with observational dat a where vaccine uptake and exposure to COVID-19 reflect their natural popu lation dynamics. The main challenge of causal attribution is bias due to h idden confounding. We will present recent advances in Statistical Science that demonstrate how credible causal attribution from observational studie s remains possible despite hidden confounding\, provided certain proxies ( even if noisy) of hidden confounders can be measured. \;As we argue\, such proxies abound in many practical settings\, examples of which we wil l describe from infectious disease\, environmental and cancer epidemiology \, providing a revealing window into the beautiful and challenging world o f causal attribution in epidemiological inquiry.
\nWhat is a Kirk D istinguished Visiting Fellow? The fellowship allows field-lea ding mathematicians from under-represented groups to join the programme as influential role models\, helping to address historical imbalances and pr omote diversity within mathematical research.
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