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Percorso della pagina
  1. Science
  2. Master Degree
  3. Informatica [F1802Q - F1801Q]
  4. Courses
  5. A.A. 2022-2023
  6. 2nd year
  1. Causal Networks
  2. Summary
Insegnamento Course full name
Causal Networks
Course ID number
2223-2-F1801Q161
Course summary SYLLABUS

Course Syllabus

  • Italiano ‎(it)‎
  • English ‎(en)‎
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Obiettivi

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.

Contenuti sintetici

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.

Programma esteso

  • Introduction to causality and why causality matters
  • The potential outcome framework; the fundamental problem of causal inference, ITE, ATE, main properties as ignorability, exchangeability, ...
  • Bayesian networks; definition, collider, chain and fork, factorization, ...
  • Causal models; do.operator, backdoor adjustement, structural causal models.
  • Randomized control trials; comparability and covariate bancing, exchangeability, no backdoor paths.
  • Nonparametric identification; frontdoor adjustment, identification from the graph structure.
  • Estimation; conditional outcome modeling, grouped conditional causal modeling, propensity score and inverse probability weighting.
  • Unobserved counfounding; no assumptions bound, optimal treatment selection, sensitivity analysis.
  • Instrumental variables; nonparametric identification of ATE, nonparametric identification of local ATE.
  • Causal discovery from observational data; constraint-based and score-based algorithms.
  • Causal discovery from interventional data; structure interventions, parametric interventions, interventional Markov equivalence.
  • Transfer learning and transportability.
  • Counterfactuals.

Prerequisiti

Basic knowledge of graph theory, optimization, probability and statistics, programming; mainly R and Python.

Modalità didattica

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 "hic sunt leones".

Materiale didattico

Slides from teachers and additional reading material.

Periodo di erogazione dell'insegnamento

First semester, in particular September 2022

Modalità di verifica del profitto e valutazione

Single assignment and final oral examination.

Orario di ricevimento

Just send me an email message at fabio.stella@unimib.it

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Aims

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.

Contents

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.

Detailed program

  • Introduction to causality and why causality matters
  • The potential outcome framework; the fundamental problem of causal inference, ITE, ATE, main properties as ignorability, exchangeability, ...
  • Bayesian networks; definition, collider, chain and fork, factorization, ...
  • Causal models; do.operator, backdoor adjustement, structural causal models.
  • Randomized control trials; comparability and covariate bancing, exchangeability, no backdoor paths.
  • Nonparametric identification; frontdoor adjustment, identification from the graph structure.
  • Estimation; conditional outcome modeling, grouped conditional causal modeling, propensity score and inverse probability weighting.
  • Unobserved counfounding; no assumptions bound, optimal treatment selection, sensitivity analysis.
  • Instrumental variables; nonparametric identification of ATE, nonparametric identification of local ATE.
  • Causal discovery from observational data; constraint-based and score-based algorithms.
  • Causal discovery from interventional data; structure interventions, parametric interventions, interventional Markov equivalence.
  • Transfer learning and transportability.
  • Counterfactuals.

Prerequisites

Basic knowledge of graph theory, optimization, probability and statistics, programming; mainly R and Python.

Teaching form

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 "hic sunt leones".

Textbook and teaching resource

Slides from teachers and additional reading material.

Semester

First semester, in particular September 2022

Assessment method

Single assignment and final oral examination.

Office hours

Just send me an email message at fabio.stella@unimib.it

Enter

Key information

Field of research
INF/01
ECTS
6
Term
First semester
Activity type
Mandatory to be chosen
Course Length (Hours)
50
Degree Course Type
2-year Master Degreee
Language
English

Staff

    Teacher

  • Fabio Antonio Stella
    Fabio Antonio Stella
  • Alessio Zanga
    Alessio Zanga

Students' opinion

View previous A.Y. opinion

Bibliography

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Enrolment methods

Manual enrolments
Self enrolment (Student)

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