Applied Causal Inference - Einzelansicht

Funktionen:

Veranstaltungsart	Vorlesung/Übung	Veranstaltungsnummer
SWS	4	Semester	SoSe 2026
Einrichtung	Institut für Informatik und Computational Science	Sprache	englisch
Belegungsfrist	01.04.2026 - 10.05.2026

Gruppe 1:

Vormerken: Zur Zeit keine Belegung möglich

		Tag	Zeit	Rhythmus	Dauer	Raum	Lehrperson	Ausfall-/Ausweichtermine	Max. Teilnehmer/-innen
	Vorlesung	Do	10:00 bis 12:00	wöchentlich	16.04.2026 bis 23.07.2026	2.70.0.08	Prof. Dr. Runge
	Übung	Do	12:00 bis 14:00	wöchentlich	16.04.2026 bis 23.07.2026	2.70.0.08	Prof. Dr. Runge

Gruppe 2:

Vormerken: Zur Zeit keine Belegung möglich

		Tag	Zeit	Rhythmus	Dauer	Raum	Lehrperson	Ausfall-/Ausweichtermine	Max. Teilnehmer/-innen
	Vorlesung	Do	10:00 bis 12:00	wöchentlich	16.04.2026 bis 23.07.2026	2.70.0.08	Prof. Dr. Runge
	Übung	Do	12:00 bis 14:00	wöchentlich	16.04.2026 bis 23.07.2026	2.70.0.09	Faltenbacher

Gruppe 3:

Vormerken: Zur Zeit keine Belegung möglich

		Tag	Zeit	Rhythmus	Dauer	Raum	Lehrperson	Ausfall-/Ausweichtermine	Max. Teilnehmer/-innen
	Vorlesung	Do	10:00 bis 12:00	wöchentlich	16.04.2026 bis 23.07.2026	2.70.0.08	Prof. Dr. Runge
	Übung	Do	12:00 bis 14:00	wöchentlich	16.04.2026 bis 23.07.2026	2.70.0.09	Lanson

Leistungsnachweis	Presentation.
Lerninhalte	<div dir="auto">This course begins with an introduction to causal inference adapted for applied science (see description below, several weeks, including practice slots). Students then work in groups to select a small project and develop a project plan over several weeks. They are supervised during the lecture and practice slots. The groups then present their project plan to the instructor and receive feedback. Following this, the projects are implemented and a poster is created. The last slots of the semester are reserved for poster presentations, with each group member presenting a portion of their project.</div><p> </p><p>Causal inference deals with the detection and quantification of causal relationships from observational data and model assumptions. A causal effect is defined as a change in a variable Y when an intervention is made in a variable X and the goal of causal inference is to learn such effects from purely observational data without manipulating the system. When quantifying causal effects, the model assumption usually consists of qualitative knowledge about causal dependencies in the form of graphs over nodes (X,Y,Z,...) with directed arrows indicating causal relationships, and the goal is to use this qualitative graph to quantify the precise influence of a node X on a node Y. When detecting causal relationships, i.e., reconstructing causal networks (graphs), more abstract assumptions about the underlying processes come into play, for example, that direct, indirect, or common-cause connections are also reflected in statistical dependencies, and vice versa. Causal inference then addresses not only algorithms that determine when a causal effect can be calculated (identified), but also the practical problem of statistical estimation. The initial lectures will focus on time series as the underlying data and will mainly explain the concepts and illustrate them with lots of Python tutorials. </p><p> </p><p>Prerequisites: Some experience with Python (loading data, plotting with matplotlib, numerical packages such as numpy and optionally sklearn and tigramite). </p>

Leistungsnachweis

Presentation.

Lerninhalte

<div dir="auto">This course begins with an introduction to causal inference adapted for applied science (see description below, several weeks, including practice slots). Students then work in groups to select a small project and develop a project plan over several weeks. They are supervised during the lecture and practice slots. The groups then present their project plan to the instructor and receive feedback. Following this, the projects are implemented and a poster is created. The last slots of the semester are reserved for poster presentations, with each group member presenting a portion of their project.</div><p> </p><p>Causal inference deals with the detection and quantification of causal relationships from observational data and model assumptions. A causal effect is defined as a change in a variable Y when an intervention is made in a variable X and the goal of causal inference is to learn such effects from purely observational data without manipulating the system. When quantifying causal effects, the model assumption usually consists of qualitative knowledge about causal dependencies in the form of graphs over nodes (X,Y,Z,...) with directed arrows indicating causal relationships, and the goal is to use this qualitative graph to quantify the precise influence of a node X on a node Y. When detecting causal relationships, i.e., reconstructing causal networks (graphs), more abstract assumptions about the underlying processes come into play, for example, that direct, indirect, or common-cause connections are also reflected in statistical dependencies, and vice versa. Causal inference then addresses not only algorithms that determine when a causal effect can be calculated (identified), but also the practical problem of statistical estimation. The initial lectures will focus on time series as the underlying data and will mainly explain the concepts and illustrate them with lots of Python tutorials. </p><p> </p><p>Prerequisites: Some experience with Python (loading data, plotting with matplotlib, numerical packages such as numpy and optionally sklearn and tigramite). </p>

Strukturbaum

Die Veranstaltung wurde 11 mal im Vorlesungsverzeichnis SoSe 2026 gefunden:

Vorlesungsverzeichnis

Mathematisch-Naturwissenschaftliche Fakultät

Institut für Geowissenschaften

Master of Science

Geosciences (Prüfungsversion ab WiSe 2022/23)

Elective Modules

GEW-ME04 - Modern Trends in Geosciences - - - 1

Geosciences (Prüfungsversion ab WiSe 2025/26)

Wahlpflichtmodule

GEW-ME04 - Modern Trends in Geosciences - - - 2

Institut für Informatik und Computational Science

Master of Science

Data Science (Prüfungsversion ab WiSe 2018/19)

Elective Modules - Advanced Module

INF-DSAM1B - Advanced Machine Learning B - - - 3

Computational Science (Prüfungsversion ab WiSe 2019/20)

I. Kernmodule Computational Science

INF-7020 - Intelligente Datenanalyse in den Naturwissenschaften - - - 4

INF-7040 - Effiziente Datenverarbeitung für die Naturwissenschaften - - - 5

Institut für Umweltwissenschaften und Geographie

Master of Science

Geoökologie (Prüfungsversion ab WiSe 2021/22)

Vertiefungsmodule

Geoökologische Ergänzung

INF-7040 - Effiziente Datenverarbeitung für die Naturwissenschaften - - - 6

Geoökologische Vertiefung

GEE-M-V12 - Spezielle Geoökologische Vertiefung - - - 7

Institut für Physik und Astronomie

Master of Science

Physik (Prüfungsversion ab WiSe 2019/20)

Wahlpflichtmodule

Außerfachliche Ergänzung

INF-7020 - Intelligente Datenanalyse in den Naturwissenschaften - - - 8

Digital Engineering Fakultät

Master of Science Computer Science (Prüfungsversion WiSe 2024/25)

Veranstaltungen - - - 9

Humanwissenschaftliche Fakultät

Department Linguistik

Master of Science

Cognitive Systems: Language, Learning and Reasoning (Prüfungsversion ab WiSe 2014/15)

Elective Modules

AM32 - Current Topics in Computational Intelligence 2 - - - 10

AM31 - Current Topics in Computational Intelligence 1 - - - 11