# Position: The Causal Revolution Needs Scientific Pragmatism

arXiv: https://arxiv.org/abs/2406.02275 · HTML: https://mlsanity.com/paper/2406.02275/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2024.

## Built on by (most cited first, 3 of 3)

- [What is causal about causal models and representations?](https://mlsanity.com/paper/2501.19335/) (2025)
- [Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption](https://mlsanity.com/paper/2508.08883/) (2025)
- [Not All Explanations for Deep Learning Phenomena Are Equally Valuable](https://mlsanity.com/paper/2506.23286/) (2025)

## Abstract (arXiv)

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.