Marco Malvaldi - Statistical Causality Asking “What if?” to data 3/6 (11/05/2021)

Data Science PhD - Marco Malvaldi - Statistical Causality Asking “What if?” to data 3/6 (11/05/2021)

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

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

Third lecture: What if I can’t measure a variable? (Tuesday 11 May, 9:00-11:00)
Building a DAG with hidden variables:
Surgery on a DAG: blocking a pipe is the best way to test where is the leakage...
Constraints in hidden variables DAG
How to establish if X causes Y in a system of n variables – some of them unknown.
First attempt: the Back-door criterion.
Second attempt: the Front-door criterion.

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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