Towards a robust approach to infer causality in molecular systems satisfying detailed balance

TL;DR

This paper investigates causality in molecular systems by analyzing asymmetric information transfer using computational methods, demonstrating that such asymmetries can indicate genuine causal relationships in equilibrium and reversible dynamics.

Contribution

It introduces a robust approach combining information transfer analysis and computational experiments to infer causality in molecular systems with detailed balance.

Findings

Asymmetric information transfer observed in molecular dynamics simulations.

Simple equilibrium systems can exhibit asymmetries similar to causal signals.

Proposed computational experiment helps distinguish genuine causality from correlation.

Abstract

The ability to distinguish between correlation and causation of variables in molecular systems remains an interesting and open area of investigation. In this work, we probe causality in a molecular system using two independent computational methods that infer the causal direction through the language of information transfer. Specifically, we demonstrate that a molecular dynamics simulation involving a single Tryptophan in liquid water displays asymmetric information transfer between specific collective variables, such as solute and solvent coordinates. Analyzing a discrete Markov-state and Langevin dynamics on a 2D free energy surface, we show that the same kind of asymmetries can emerge even in extremely simple systems, undergoing equilibrium and time-reversible dynamics. We use these model systems to rationalize the unidirectional information transfer in the molecular system in terms of asymmetries in the underlying free energy landscape and/or relaxation dynamics of the relevant coordinates. Finally, we propose a computational experiment that allows one to decide if an asymmetric information transfer between two variables corresponds to a genuine causal link.