Ayana Ghosh Computational Sciences and Engineering Division, Oak Ridge National Laboratory *Incoming faculty at Department of Physics, Indian Institute of Technology, Madras, India, Causal reasoning has become a powerful paradigm in disciplines such as statistics, economics, epidemiology, enabling robust interpretation, intervention, and prediction beyond correlation-based analysis. Its systematic adoption in the physical sciences, particularly in theoretical condensed matter physics or materials science, remains limited. This presentation introduces causal reasoning models tailored for functional materials, aimed at uncovering atomistic cause-effect mechanisms underlying emergent phenomena. We focus on complex material systems including perovskite oxides, two-dimensional layered materials, and heterostructures, which display rich design spaces arising from compositional variability, lattice distortions, supercell periodicity, or electronic characteristics. These features, while offering tunability, complicate mechanistic interpretation. By integrating atomistic simulations with causal reasoning, we link local structural distortions, defects, or external stimuli such as temperature or electric fields to domain formation, evolution, or polarization dynamics. Causally informed AI/machine learning aims to establish closed-loop feedback between simulations and experiments, permitting targeted interventions to optimize functional responses.