2023/06/29 by Simon Ferreira, Ferreira, Simon, Assaad, Charles K. · 6 citations
Computer Science · Biochemistry, Genetics and Molecular Biology · #Bayesian Modeling and Causal Inference #Bioinformatics and Genomic Networks #Advanced Graph Neural Networks
paper · pdf · doi:10.48550/arxiv.2306.16958
Dynamic structural causal models (SCMs) are a powerful framework for reasoning in dynamic systems about direct effects which measure how a change in one variable affects another variable while holding all other variables constant. The causal relations in a dynamic structural causal model can be qualitatively represented with an acyclic full-time causal graph. Assuming linearity and no hidden confounding and given the full-time causal graph, the direct causal effect is always identifiable. However, in many application such a graph is not available for various reasons but nevertheless experts have access to the summary causal graph of the full-time causal graph which represents causal relations between time series while omitting temporal information and allowing cycles. This paper presents a complete identifiability result which characterizes all cases for which the direct effect is graphically identifiable from a summary causal graph and gives two sound finite adjustment sets that can be used to estimate the direct effect whenever it is identifiable.