Upcoming Meeting on November 23, 2023
- November 23, 2023 from 14.30 to 17.30 at NU-5A47 on the VU Campus.
|14:30-15:30||Johannes Textor (RU, RUMC) - Are DAGs Really Useful for Causal Reasoning in Complex Systems?|
Much causal inference methodology is based on the assumption that the world can be neatly and meaningfully described by DAGs or similar formalisms. I will explore the use of simulation to investigate when and how this assumption breaks down when facing complex systems with emergent properties. Specifically, I will discuss a simple biophysical simulation model of a cell for which we have both complete knowledge of the computation graph and a good understanding of its emergent properties and phenomenology. Using this system as an example, I will illustrate the conceptual and ontological problems that arise when attempting to formulate causal processes in terms of graphical models whose nodes represent high-level descriptions of emergent phenomena. I argue that these problems are similar to those resulting from the use of social categories as nodes in DAGs, and that they cannot be easily addressed by existing extensions of the DAG framework.
|15:30-16:30||Julia Kowalska (Amsterdam UMC) - Regression Discontinuity Design from Bayesian perspective: opportunities and challenges (slides)|
Regression discontinuity design (RDD) is a quasi-experimental design that aims at the causal effect estimation of an intervention that is assigned based on a cutoff criterion. Regression discontinuity design exploits the idea that close to the cutoff units below and above the cutoff are similar hence can be meaningfully compared. However, the causal effect can be estimated only locally at the cutoff point. In many experiments, the cutoff criterion serves only as a guideline rather than a strict rule. Nonetheless, the guidelines may not be publicly known, or the cutoff used in practice is shifted with respect to the official one. If the analysis is performed at a false cutoff point, it leads to meaningless results, but as the intervention assignment is binary, the location of the cutoff may be unclear. Through the Bayesian approach, we can incorporate prior knowledge and uncertainty about the cutoff location in the causal effect estimation. At the same time, RDD is a boundary point estimation problem, whereas the Bayesian model is fitted to the whole data. Therefore, a natural challenge arises: how to make Bayesian inference more local?
Meetings dates to be determined later.