Complex polynomials are one of the oldest and most fundamental objects of study in mathematics, and are ubiquitous in applications.
As the fields of statistics and data science have grown, the importance of reproducibility in research and easing the “replication crisis” has become increasingly apparent. The inability to reproduce scientific results when using the same data and code may lead to a lack of confidence in the validity of research and can make it difficult to build on and advance scientific knowledge.
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.