Conducting Causal Analysis by Means of Approximating Probabilistic Truths

Bo Pieter Johannes Andree

doi:10.3390/books978-3-0365-8051-7

Conducting Causal Analysis by Means of Approximating Probabilistic Truths

Bo Pieter Johannes Andree

Spatial Economics

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

Abstract

The current paper develops a probabilistic theory of causation and suggests
practical routines for conducting causal inference applicable to new machine learning methods that have, so far, remained relatively underutilized in this context.

Original language	English
Title of host publication	Causal Inference for Heterogeneous Data and Information Theory
Subtitle of host publication	Reprinted from: Entropy 2022, 24, 92, doi:10.3390/e24010092
Editors	Katerina Hlavackova-Schindler
Place of Publication	Basel
Publisher	Multidisciplinary Digital Publishing Institute (MDPI)
Chapter	14
Pages	249-272
ISBN (Electronic)	978-3-0365-8051-7
ISBN (Print)	978-3-0365-8050-0
DOIs	https://doi.org/10.3390/books978-3-0365-8051-7
Publication status	Published - 17 Jul 2023

Keywords

Causal Inference
Information Theory
Heterogeneous Data
Machine Learning
Time Series
Dynamical Systems
Instrument Variables
Treatment Effects

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10.3390/books978-3-0365-8051-7

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Andree, B. P. J. (2023). Conducting Causal Analysis by Means of Approximating Probabilistic Truths. In K. Hlavackova-Schindler (Ed.), Causal Inference for Heterogeneous Data and Information Theory: Reprinted from: Entropy 2022, 24, 92, doi:10.3390/e24010092 (pp. 249-272). Article 13 Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/books978-3-0365-8051-7

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keywords = "Causal Inference, Information Theory, Heterogeneous Data, Machine Learning, Time Series, Dynamical Systems, Instrument Variables, Treatment Effects",

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Andree, BPJ 2023, Conducting Causal Analysis by Means of Approximating Probabilistic Truths. in K Hlavackova-Schindler (ed.), Causal Inference for Heterogeneous Data and Information Theory: Reprinted from: Entropy 2022, 24, 92, doi:10.3390/e24010092., 13, Multidisciplinary Digital Publishing Institute (MDPI), Basel, pp. 249-272. https://doi.org/10.3390/books978-3-0365-8051-7

Conducting Causal Analysis by Means of Approximating Probabilistic Truths. / Andree, Bo Pieter Johannes.
Causal Inference for Heterogeneous Data and Information Theory: Reprinted from: Entropy 2022, 24, 92, doi:10.3390/e24010092. ed. / Katerina Hlavackova-Schindler. Basel: Multidisciplinary Digital Publishing Institute (MDPI), 2023. p. 249-272 13.

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

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KW - Information Theory

KW - Heterogeneous Data

KW - Machine Learning

KW - Time Series

KW - Dynamical Systems

KW - Instrument Variables

KW - Treatment Effects

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Conducting Causal Analysis by Means of Approximating Probabilistic Truths

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Keywords

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Conducting Causal Analysis by Means of Approximating Probabilistic Truths

Theory and Application of Dynamic Spatial Time Series Models

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