Conducting Causal Analysis by Means of Approximating Probabilistic Truths
The current paper develops a probabilistic theory of causation using measure-theoretical concepts and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models. An example is provided using random forest regressions and...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/1/92 |
| _version_ | 1797494231623794688 |
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| author | Bo Pieter Johannes Andrée |
| author_facet | Bo Pieter Johannes Andrée |
| author_sort | Bo Pieter Johannes Andrée |
| collection | DOAJ |
| description | The current paper develops a probabilistic theory of causation using measure-theoretical concepts and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models. An example is provided using random forest regressions and daily data on yield spreads. The application tests how uncertainty in short- and long-term inflation expectations interacts with spreads in the daily Bitcoin price. The results are contrasted with those obtained by standard linear Granger causality tests. It is shown that the suggested measure-theoretic approaches do not only lead to better predictive models, but also to more plausible parsimonious descriptions of possible causal flows. The paper concludes that researchers interested in causal analysis should be more aspirational in terms of developing predictive capabilities, even if the interest is in inference and not in prediction per se. The theory developed in the paper provides practitioners guidance for developing causal models using new machine learning methods that have, so far, remained relatively underutilized in this context. |
| first_indexed | 2024-03-10T01:31:23Z |
| format | Article |
| id | doaj.art-20e3bf72f2144fd3ba3b3d48894c80cd |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T01:31:23Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-20e3bf72f2144fd3ba3b3d48894c80cd2023-11-23T13:41:45ZengMDPI AGEntropy1099-43002022-01-012419210.3390/e24010092Conducting Causal Analysis by Means of Approximating Probabilistic TruthsBo Pieter Johannes Andrée0Analytics and Tool Unit, Development Economics Data Group, World Bank, 1818 H St NW, Washington, DC 20433, USAThe current paper develops a probabilistic theory of causation using measure-theoretical concepts and suggests practical routines for conducting causal inference. The theory is applicable to both linear and high-dimensional nonlinear models. An example is provided using random forest regressions and daily data on yield spreads. The application tests how uncertainty in short- and long-term inflation expectations interacts with spreads in the daily Bitcoin price. The results are contrasted with those obtained by standard linear Granger causality tests. It is shown that the suggested measure-theoretic approaches do not only lead to better predictive models, but also to more plausible parsimonious descriptions of possible causal flows. The paper concludes that researchers interested in causal analysis should be more aspirational in terms of developing predictive capabilities, even if the interest is in inference and not in prediction per se. The theory developed in the paper provides practitioners guidance for developing causal models using new machine learning methods that have, so far, remained relatively underutilized in this context.https://www.mdpi.com/1099-4300/24/1/92causalityBitcoininflationyield spreadsapproximation theoryHellinger distance |
| spellingShingle | Bo Pieter Johannes Andrée Conducting Causal Analysis by Means of Approximating Probabilistic Truths Entropy causality Bitcoin inflation yield spreads approximation theory Hellinger distance |
| title | Conducting Causal Analysis by Means of Approximating Probabilistic Truths |
| title_full | Conducting Causal Analysis by Means of Approximating Probabilistic Truths |
| title_fullStr | Conducting Causal Analysis by Means of Approximating Probabilistic Truths |
| title_full_unstemmed | Conducting Causal Analysis by Means of Approximating Probabilistic Truths |
| title_short | Conducting Causal Analysis by Means of Approximating Probabilistic Truths |
| title_sort | conducting causal analysis by means of approximating probabilistic truths |
| topic | causality Bitcoin inflation yield spreads approximation theory Hellinger distance |
| url | https://www.mdpi.com/1099-4300/24/1/92 |
| work_keys_str_mv | AT bopieterjohannesandree conductingcausalanalysisbymeansofapproximatingprobabilistictruths |