Causal Inference

In the noisy world of algorithmic trading, volatility is often treated as something to manage or hedge against. But what if it could be a signal generator? Ivan Letteri’s recent paper proposes a novel trading framework that does just that: it treats mid-range volatility not as a nuisance, but as the key to unlocking directional causality between assets. From Volatility to Causality: The 4-Step Pipeline This is not your standard volatility arbitrage. The author introduces a four-stage pipeline that transforms volatility clusters into trading signals: ...

“In the absence of perfect knowledge, how do we still reason causally?” This paper tackles a profound and practical dilemma in causal inference: what if we don’t know the full causal graph? In real-world settings — whether in healthcare, finance, or digital platforms — complete causal diagrams are rare. Practitioners instead rely on causal abstractions: simplified, coarse-grained representations that preserve partial causal knowledge. But this raises a fundamental question: Which causal queries can still be identified under such abstraction? ...