Ever wondered why your BTC strategy crumbles the moment the market regime flips — even though your backtest looked great? The usual culprit is correlation masquerading as causation. In crypto, narratives rotate, liquidity migrates across chains, and perp funding whipsaws. If your model chases yesterday’s correlations, tomorrow’s volatility will humble it.
This article turns a recent research idea — group causal inference with deep learning and knockoff interventions — into a crypto builder’s playbook. You’ll learn how to test groups of crypto signals (e.g., on‑chain demand, derivatives, macro) for causal influence on an asset’s returns, and you’ll get runnable Python to try it yourself.
Most “causal” tools compare two variables at a time (A → B). But crypto ecosystems are teams, not solo acts. On‑chain demand isn’t just one series; it’s a bundle — fees, active addresses, DEX volume, validator yield, L2 throughput. Derivatives data is another bundle — funding, basis, open interest. Group causality asks: does this whole group move the needle on our target (BTC/ETH returns) when we intervene on it?
