Position: The Causal Revolution Needs Scientific Pragmatism

Joshua R. Loftus
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:32671-32679, 2024.

Abstract

Causal models and methods have great promise, but their progress has been stalled. Proposals using causality get squeezed between two opposing worldviews. Scientific perfectionism–an insistence on only using “correct” models–slows the adoption of causal methods in knowledge generating applications. Pushing in the opposite direction, the academic discipline of computer science prefers algorithms with no or few assumptions, and technologies based on automation and scalability are often selected for economic and business applications. We argue that these system-centric inductive biases should be replaced with a human-centric philosophy we refer to as scientific pragmatism. The machine learning community must strike the right balance to make space for the causal revolution to prosper.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-loftus24a, title = {Position: The Causal Revolution Needs Scientific Pragmatism}, author = {Loftus, Joshua R.}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {32671--32679}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/loftus24a/loftus24a.pdf}, url = {https://proceedings.mlr.press/v235/loftus24a.html}, abstract = {Causal models and methods have great promise, but their progress has been stalled. Proposals using causality get squeezed between two opposing worldviews. Scientific perfectionism–an insistence on only using “correct” models–slows the adoption of causal methods in knowledge generating applications. Pushing in the opposite direction, the academic discipline of computer science prefers algorithms with no or few assumptions, and technologies based on automation and scalability are often selected for economic and business applications. We argue that these system-centric inductive biases should be replaced with a human-centric philosophy we refer to as scientific pragmatism. The machine learning community must strike the right balance to make space for the causal revolution to prosper.} }
Endnote
%0 Conference Paper %T Position: The Causal Revolution Needs Scientific Pragmatism %A Joshua R. Loftus %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-loftus24a %I PMLR %P 32671--32679 %U https://proceedings.mlr.press/v235/loftus24a.html %V 235 %X Causal models and methods have great promise, but their progress has been stalled. Proposals using causality get squeezed between two opposing worldviews. Scientific perfectionism–an insistence on only using “correct” models–slows the adoption of causal methods in knowledge generating applications. Pushing in the opposite direction, the academic discipline of computer science prefers algorithms with no or few assumptions, and technologies based on automation and scalability are often selected for economic and business applications. We argue that these system-centric inductive biases should be replaced with a human-centric philosophy we refer to as scientific pragmatism. The machine learning community must strike the right balance to make space for the causal revolution to prosper.
APA
Loftus, J.R.. (2024). Position: The Causal Revolution Needs Scientific Pragmatism. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:32671-32679 Available from https://proceedings.mlr.press/v235/loftus24a.html.

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