Unifying Gaussian LWF and AMP Chain Graphs to Model Interference
This work addresses the challenge of interference in causal inference, which is often unmodeled, providing a theoretical foundation for researchers in statistics and machine learning, though it appears incremental as it builds on existing chain graph models.
The paper tackled the problem of modeling interference in causal relationships by proposing a new class of Gaussian chain graphs that unify LWF and AMP frameworks, enabling representation of both interference and non-interference, and introduced properties, an estimation algorithm, and intervention computation methods.
An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian distributions. Specifically, we define the new class of models, introduce global and local and pairwise Markov properties for them, and prove their equivalence. We also propose an algorithm for maximum likelihood parameter estimation for the new models, and report experimental results. Finally, we show how to compute the effects of interventions in the new models.