4 accepted papers
Graphical models have been widely used as parsimonious encoders of constraints of the underlying probability models. When organized in a structured way, these models can facilitate the derivation of non-trivial constraints, the inference of quantities of interest, and the optimization of their estim…
Generalizing causal knowledge across environments is a common challenge shared across many of the data-driven disciplines, including AI and ML. Experiments are usually performed in one environment (e.g., in a lab, on Earth, in a training ground), almost invariably, with the intent of being used else…
The Ladder of Causation describes three qualitatively different types of activities an agent may be interested in engaging in, namely, seeing (observational), doing (interventional), and imagining (counterfactual) (Pearl and Mackenzie, 2018). The inferential challenge imposed by the causal hierarchy…
We study the problem of causal identification from an arbitrary collection of observational and experimental distributions, and substantive knowledge about the phenomenon under investigation, which usually comes in the form of a causal graph. We call this problem \textit{g-identifiability}, or gID…