Inference of the Kinetic Ising Model with Heterogeneous Missing Data

TL;DR

This paper introduces a pseudo-EM algorithm based on path integral methods for inferring causality structures in binary time series with missing data, effective even under severe data sparsity.

Contribution

It develops a novel inference method combining path integral and saddle-point approximation for the Kinetic Ising Model with missing data, including a recursive version for improved accuracy.

Findings

Effective in high missing data scenarios

Performance depends on observation heterogeneity

Robust to certain model assumption violations

Abstract

We consider the problem of inferring a causality structure from multiple binary time series by using the Kinetic Ising Model in datasets where a fraction of observations is missing. We take our steps from a recent work on Mean Field methods for the inference of the model with hidden spins and develop a pseudo-Expectation-Maximization algorithm that is able to work even in conditions of severe data sparsity. The methodology relies on the Martin-Siggia-Rose path integral method with second order saddle-point solution to make it possible to calculate the log-likelihood in polynomial time, giving as output a maximum likelihood estimate of the couplings matrix and of the missing observations. We also propose a recursive version of the algorithm, where at every iteration some missing values are substituted by their maximum likelihood estimate, showing that the method can be used together with sparsification schemes like LASSO regularization or decimation. We test the performance of the algorithm on synthetic data and find interesting properties when it comes to the dependency on heterogeneity of the observation frequency of spins and when some of the hypotheses that are necessary to the saddle-point approximation are violated, such as the small couplings limit and the assumption of statistical independence between couplings.