Bruno Petrungaro, Anthony C. Constantinou
arXiv 9 Feb 2026 · Machine Learning
arXiv:2603.00041 · PDF · DOI · OpenAlex · Extracted main text
Causal machine learning (ML) recovers graphical structures that inform us about potential cause-and-effect relationships. Most progress has focused on cross-sectional data with no explicit time order, whereas recovering causal structures from time series data remains the subject of ongoing research in causal ML. In addition to traditional causal ML, this study assesses econometric methods that some argue can recover causal structures from time series data. The use of these methods can be explained by the significant attention the field of econometrics has given to causality, and specifically to time series, over the years. This presents the possibility of comparing the causal discovery performance between econometric and traditional causal ML algorithms. We seek to understand if there are lessons to be incorporated into causal ML from econometrics, and provide code to translate the results of these econometric methods to the most widely used Bayesian Network R library, bnlearn. We investigate the benefits and challenges that these algorithms present in supporting policy decision-making, using the real-world case of COVID-19 in the UK as an example. Four econometric methods are evaluated in terms of graphical structure, model dimensionality, and their ability to recover causal effects, and these results are compared with those of eleven causal ML algorithms. Amongst our main results, we see that econometric methods provide clear rules for temporal structures, whereas causal-ML algorithms offer broader discovery by exploring a larger space of graph structures that tends to lead to denser graphs that capture more identifiable causal relationships.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Constantinou, A. and others (2023) Open problems in causal structure learning: A case study of COVID-19 in the UK self | 1.000 | 11 | 3 | 100% |
| 2 | Scutari, M (2010) Learning Bayesian Networks with the bnlearn R Package | 1.000 | 8 | 3 | 100% |
| 3 | Tsamardinos, I. and others (2006) The max-min hill-climbing Bayesian network structure learning algorithm | 0.644 | 2 | 2 | 100% |
| 4 | Pearl, J (1995) Causal diagrams for empirical research | 0.644 | 2 | 2 | 100% |
| 5 | Athey, Susan and Imbens, Guido W (2017) The State of Applied Econometrics: Causality and Policy Evaluation | 0.585 | 3 | 1 | 100% |
| 6 | Meinshausen, Nicolai and Bühlmann, Peter (2006) Variable selection and high-dimensional graphs with the lasso | 0.585 | 3 | 1 | 100% |
| 7 | Hastie, T. and Efron, B lars: Least Angle Regression, Lasso and Forward Stagewise | 0.585 | 3 | 1 | 100% |
| 8 | Petrungaro, Bruno and Kitson, Neville K and Constantinou, Anthony C (2025) Investigating potential causes of Sepsis with Bayesian network structure learning self | 0.511 | 2 | 1 | 100% |
| 9 | Tsamardinos, I. and others (2003) Algorithms for large scale Markov blanket discovery. | 0.511 | 2 | 1 | 100% |
| 10 | Bouckaert, Remco Ronaldus (1995) Bayesian belief networks: from construction to inference | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 49 scored citations.