The course aims to provide a gentle introduction to causal inference and in particular to causal newtworks and structural causal models.
In particular, the course gives strong motivations because, at the current state-of-the-art, modern machine learning experts need causality, and tools from causal modeling, to correctly address and effectively solve problems of decision making under uncertainty.
Main contents are as follows; the potential outcome framework, main definitions and properties of probabilistic graphical models with specific reference to Bayesian networks, causal networks and structural causal models, randomized experiments, nonparametric identification of causal effect, estimation of causal effect, unobserved confounding, instrumental variables, structural learning from observational data and from observational and intervention data, basic concepts of tranfer learning and transportability, and finally a basic introduction to counterfactuals.
Basic knowledge of graph theory, optimization, probability and statistics, programming; mainly R and Python.
The course is expected to be delivered in presence, even if at the current stage of knowledge, due to the pandemic, we could well say "ics sunt leones".
Textbook and teaching resource
Slides from teachers, reading material and main textbooks related to causal inference and causal networks, to be announced soon.