"Causal Discovery in Python" - Lizzie Silver (Pycon AU 2024)

(Lizzie Silver) A review and comparison of software available for causal discovery in Python. Causal discovery means learning "what causes what" from your data. The input is a tabular dataset; the output is a causal graphical model (or a set of potential models) over your features. If feature A affects feature B, there should be an arrow A-→B in the causal graphical model. Causal discovery is useful for hypothesis generation, experiment selection, and for testing our assumptions around causation.
I'll give a brief intro to causal discovery, then review the following packages: py-tetrad, causal-learn, tigramite, causalnex, and cdt (causal discovery toolbox). The packages have some overlap but different emphases: each one implements at least one algorithm not covered by the other packages, making them useful in different situations. If time permits I'll finish with a quick demo, showing each package learning a model from the same dataset.
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Fri Nov 22 09:20:00 2024 at Door 12 / Goldfields Theatre