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Unifying Gaussian LWF and AMP Chain Graphs to Model Interference

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  • Peña Jose M.

    (IDA, Linköping University, Linköping, Sweden)

Abstract

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.

Suggested Citation

  • Peña Jose M., 2020. "Unifying Gaussian LWF and AMP Chain Graphs to Model Interference," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 1-21, January.
    • Handle: RePEc:bpj:causin:v:8:y:2020:i:1:p:1-21:n:3
    DOI: 10.1515/jci-2018-0034
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    References listed on IDEAS

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    1. Mathias Drton & Michael Eichler, 2006. "Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(2), pages 247-257, June.
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