Reducing Causal Illusions through Deliberate Undersampling

Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey Plis

Keywords: Causal learning, causal structure, undersampled time-series, deliberate undersampling, non-monotonic undersampling

TL;DR: Estimate of the causal structure of a dynamical system can improve when adding data collected at a slower sampling rate: an algorithm, and synthetic demonstrations.

Abstract:

Domain scientists interested in the causal mechanisms are usually limited by the frequency at which they can collect the measurements of social, physical, or biological systems.
It is a reasonable assumption that higher frequency is more informative of the causal structure.
This assumption is a strong driver for designing new, faster instruments.
A task that is expensive and often impossible at the current state of technology.
In this work, we show that counter to the intuition it is possible for causal systems to improve the estimation of causal graphs from undersampled time-series by augmenting the measurements with those collected at a rate slower than currently available.
We present an algorithm able to take advantage of measurement time-scale graphs estimated from data at various sampling rates and lower the underdeterminacy of the system by reducing the equivalence size.
We investigate the probability of cases in which deliberate undersampling yields a gain and the size of this gain.