Uploaded December 2024 | Updated September 2026, 5 hours ago
(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.
pretalx.com/pycon-au-2024/talk/ACKK3A
python, pycon, australia, programming, conference, technical, developers, panel, sessions, libraries, frameworks, community, sysadmins, students, education, data, science
Videos licensed as CC-BY-NC-SA 4.0
PyCon AU is the national conference for the Python programming community, bringing together professional, student and enthusiast developers, sysadmins and operations folk, students, educators, scientists, statisticians, and many others besides, all with a love for working with Python.
Licensed as CC BY-NC-SA - creativecommons.org/licenses/by-nc-sa/4.0
Produced by Next Day Video Australia: https://nextdayvideo.com.au
Fri Nov 22 09:20:00 2024 at Door 12 / Goldfields Theatre
(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.
pretalx.com/pycon-au-2024/talk/ACKK3A
python, pycon, australia, programming, conference, technical, developers, panel, sessions, libraries, frameworks, community, sysadmins, students, education, data, science
Videos licensed as CC-BY-NC-SA 4.0
PyCon AU is the national conference for the Python programming community, bringing together professional, student and enthusiast developers, sysadmins and operations folk, students, educators, scientists, statisticians, and many others besides, all with a love for working with Python.
Licensed as CC BY-NC-SA - creativecommons.org/licenses/by-nc-sa/4.0
Produced by Next Day Video Australia: https://nextdayvideo.com.au
Fri Nov 22 09:20:00 2024 at Door 12 / Goldfields Theatre
