The causal inference trap and digging out with far-out-of-equilibrium sampling

Abhranil’s work on the intrinsic difficulty of inferring connectivity from activity in strongly recurrent networks (read: highly correlated variables) just accepted for publication (Nat. Neurosci March 2020; bioRxiv, Jan 2019)!

We show that attempting causal inference in systems with strongly correlated variables will typically result in an overestimation of connectivity, drawing causal connections where there are none.

Even in fully observed systems, this failure to explain away correlations occurs when there is mismatch between the dynamical model generating the data and the statistical model doing the inference, an inevitable situation in the real world.

Thus, causal inference from activity is especially problemmatic in systems with strong correlations.

In short, not only are correlations not causation, correlations hurt causation.

The good news: We find that sampling data shortly after kicking the system far-out-of-equilibrium by low-dimensional suppressive drive could mitigate the inference problem without the need for inducing and tracking detailed high-dimensional (holographic) perturbations.

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