Causal Direction from Convergence Time: Faster Training in the True Causal Direction
#Causal Direction #Neural Networks #Optimization Dynamics #Causal Computational Asymmetry #Convergence Time #Machine Learning #Causal Inference
📌 Key Takeaways
- CCA identifies causal direction based on neural network training convergence rates
- The true causal direction converges faster during optimization due to favorable gradient dynamics
- CCA operates in optimization-time space, distinguishing it from statistical methods
- The approach achieved high accuracy (26/30) across various neural architectures
- CCA is integrated into a broader framework called Causal Compression Learning
📖 Full Retelling
Researcher Abdulrahman Tamim introduced Causal Computational Asymmetry (CCA), a novel principle for causal direction identification in machine learning, through a paper published on arXiv on February 24, 2026, addressing the challenge of determining cause-effect relationships in complex systems. The CCA principle works by training two neural networks simultaneously - one to predict variable Y from X and another to predict X from Y, with the direction that demonstrates faster convergence during training being inferred to be the true causal direction. This approach operates in optimization-time space, distinguishing it from existing methods like RESIT, IGCI, and SkewScore that rely on statistical independence or distributional asymmetries. The method requires proper z-scoring of both variables to ensure valid comparison of convergence rates. Under the additive noise model Y = f(X) + ε with ε independent of X and f being nonlinear and injective, the research establishes a formal asymmetry where the true causal direction converges faster during optimization due to lower irreducible loss and more favorable gradient dynamics. The paper demonstrates CCA achieving 26/30 correct causal identifications across six neural architectures, with perfect 30/30 results on sine and exponential data-generating processes.
🏷️ Themes
Machine Learning, Causal Inference, Optimization Dynamics
📚 Related People & Topics
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Original Source
--> Computer Science > Machine Learning arXiv:2602.22254 [Submitted on 24 Feb 2026] Title: Causal Direction from Convergence Time: Faster Training in the True Causal Direction Authors: Abdulrahman Tamim View a PDF of the paper titled Causal Direction from Convergence Time: Faster Training in the True Causal Direction, by Abdulrahman Tamim View PDF HTML Abstract: We introduce Causal Computational Asymmetry , a principle for causal direction identification based on optimization dynamics in which one neural network is trained to predict $Y$ from $X$ and another to predict $X$ from $Y$, and the direction that converges faster is inferred to be causal. Under the additive noise model $Y X) + \varepsilon$ with $\varepsilon \perp X$ and $f$ nonlinear and injective, we establish a formal asymmetry: in the reverse direction, residuals remain statistically dependent on the input regardless of approximation quality, inducing a strictly higher irreducible loss floor and non-separable gradient noise in the optimization dynamics, so that the reverse model requires strictly more gradient steps in expectation to reach any fixed loss threshold; consequently, the forward direction converges in fewer expected optimization steps. CCA operates in optimization-time space, distinguishing it from methods such as RESIT, IGCI, and SkewScore that rely on statistical independence or distributional asymmetries, and proper z-scoring of both variables is required for valid comparison of convergence rates. On synthetic benchmarks, CCA achieves 26/30 correct causal identifications across six neural architectures, including 30/30 on sine and exponential data-generating processes. We further embed CCA into a broader framework termed Causal Compression Learning , which integrates graph structure learning, causal information compression, and policy optimization, with all theoretical guarantees formally proved and empirically validated on synthetic datasets. Subjects: Machine Learning (cs.LG) ; Artificia...
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