Causal Inference in Energy Demand Prediction
TL;DR Structural causal models outperform traditional energy forecasts by revealing critical interdependencies correlation-based approaches fail to capture.
Causify Blog#
A Benchmark of Causal vs Correlation AI for Predictive Maintenance
TL;DR Causal AI achieved $49,500 annual economic advantage over best ML baseline with 93.9% recall through explicit modeling of failure mechanisms rather than relying on statistical correlations
A Look at Runnable Directories: The Solution to the Monorepo vs Multi-repo Debate
Ask any engineering team that has scaled past a few dozen developers: how do you organize your code? The answer shapes everything from build times to team velocity to system reliability.
The industry settled into two camps. Monorepos offer consistency but struggle with scale. Multi-repos provide independence but create coordination overhead. Both work. Neither solves the underlying trade-offs between modularity and unified workflows.
Causal AI: The Next Generation of Predictive Maintenance
Your predictive maintenance system catches failures—but it also generates dozens of false alarms. Your maintenance teams are skeptical. The ROI is unclear. A new benchmark study of 10,000 CNC machines reveals why: traditional ML optimizes for statistical accuracy instead of business outcomes.
The cost? Approximately $80,000 per 2,000 machines annually.
How We Ask for Feedback at Causify
Asking for feedback can feel awkward, and giving it can be just as uncomfortable.
In many companies, the feedback process can devolve into struggles over punctuation or vague critiques that don't provide real value. Conversely, receiving requests for feedback often means wading through poorly constructed documents or hastily generated AI content, combined with unrealistic deadlines.