For your research, if you've ever wanted to understand and model causal effects in a rigorous way that reflects the complexity of the real-world. NCA understands a cause as a necessary (but not sufficient) condition, rather than a probabilistic cause (as in regression analysis). “Necessary” means that a certain outcome will not occur without a … Continue reading Introduction to Necessary Condition Analysis (NCA) →

This workshop offers an intensive 4-day introduction to causal modeling with Coincidence Analysis (CNA), a relatively new configurational comparative method of data analysis geared towards causal complexity, which has seen a considerable uptick in applications in recent years. No prior knowledge of CNA is required. In plenary lectures, the main developer of CNA, Michael Baumgartner, and … Continue reading Introduction to Causal Data Analysis and Modeling with Coincidence Analysis →