Marco Malvaldi - Statistical Causality Asking “What if?” to data 4/6 (14/05/2021)

Data Science PhD - Marco Malvaldi - Statistical Causality Asking “What if?” to data 4/6 (14/05/2021)

Short course for the PhD Program in Data Science – Scuola Normale Superiore

Title: Statistical Causality – Asking “What if?” to data
Lecturer: Marco Malvaldi

Fourth lecture: Let’s generalize (Friday 14 May, 9:00-11:00)
General methods for operating on DAGs
A general criterion: if no child of X can be reached with a bidirectional trajectory, we can state if X causes, Y, that is, we can always express p(Y|do(X)) as a linear combination of conditional probabilities, and the procedure can be cast into an algorithm.

Causal Inference in Statistics by Pearl, Glamour, Jewell
Causality by Pearl
Elements of causal Inference by Janzing, Peters, Scholkopf
On Identifying Causal Effects by Tian, Shpitser
The Do-Calculus Revisited by Pearl
A General Identification Condition for Causal Effects by Tian, Pearl
DAGs: Overview and Motivation by Elwert
Graphical Causal Models by Elwert
Probabilistic evaluation of counterfactual queries by Balke, Pearl
Direct and Indirect Effects, by Pearl
Simpson’s paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects, by von Kugelgen, Gresele, Scholkopf

About the instructor:
Marco Malvaldi is a popular Italian author of novels, short stories and scientific essays for the general public on popularizing physics-chemistry, computing, mathematics and the scientific aspects of human communication. Malvaldi holds a PhD in chemistry from the University of Pisa (2004) and, beyond a novelist and a teacher, is also an active researcher
in physical chemistry and in sport data analytics. Malvaldi is currently a research collaborator of the department of Computer Science, University of Pisa, focused on causal reasoning, within the European project “Humane-AI-Net”.

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