Pinpointing Causality across Time and Geography: Uncovering the Relationship between Airstrikes and Insurgent Violence in Iraq
“Correlation is not causation”, as the saying goes, yet sometimes it can be, if certain assumptions are met. Describing those assumptions and developing methods to estimate causal effects, not just correlations, is the central concern of the causal inference field. Broadly speaking, causal inference seeks to measure the effect of a treatment on an outcome. This treatment can be an actual medicine or something more abstract like a policy. Much of the literature in this space focuses on relatively simple treatments/outcomes and uses data which doesn’t exhibit much dependency. As an example, clinicians often want to measure the effect of a binary treatment (received the drug or not) on a binary outcome (developed the disease or not). The data used to answer such questions is typically patient-level data where the patients are assumed to be independent from each other. To be clear, these simple setups are enormously useful and describe commonplace causal questions.
How Statistics Can Save Lives in a Pandemic
In responding to a pandemic, time is of the essence. As the COVID-19 pandemic has raged on, it has become evident that complex decisions must be made as quickly as possible, and quality data and statistics are necessary to drive the solutions that can prevent mass illness and death. Therefore, it is essential to outline a robust and generalizable statistical process that can not only help to diminish the current COVID-19 pandemic but also assist in the prevention of potential future pandemics.