The eight basic rules for causal inference from Peder M. Isager’s blog provide an excellent framework for understanding how causal relationships emerge and interact with observable data. These rules resonate with core principles in both systems thinking and Ashtanga Yoga. Let’s explore this connection. 1. Independent Variables are Not Correlated In systems thinking, an independent variable can be seen as an external input or a system component that does not directly influence other parts. This rule aligns with the yogic principle of detachment (Vairagya) where actions devoid of expectations (causal independence) do not entangle one in their outcomes. 2. Causal Influence Creates Correlation This mirrors the idea in systems thinking that feedback loops (positive or negative) generate observable patterns. In yoga, cause-effect chains are understood…

In systems thinking, a collider is a variable influenced by two or more independent factors—and Ashtanga’s authorization system contains a perfect example that explains why the current model is so resistant to change. Consider the collider: “Becoming an Authorized Teacher” This outcome is influenced by two largely independent variables: In an ideal system, both would be required for authorization. But the current Ashtanga authorization process primarily conditions on X (asana achievement), creating what statisticians call collider bias—a systematic distortion that produces misleading correlations. Here’s how the collider creates dysfunction: The Selection Bias When we only examine people who’ve achieved authorization (conditioning on the collider), we’re looking at a pre-filtered population. Within this group, we might observe that teachers with exceptional asana ability sometimes struggle with…