Identifying the critical controls on microbial methane (CH4) production in permafrost systems is essential to constrain current biogeochemical models in a warming world. Yet quantitatively analyzing the relative importance of all model factors involved in methane production reaction networks is rare. Here, we applied causal inference, root cause analysis, and network analysis to microbial reaction networks to explore the contribution of each factor to variations in methane production and pathways. We first derived time series data of soil temperature, substrates, microbial biomass, etc., using simulations of BioCrunch, a genome-informed reactive transport model, at a fen site at Stordalen Mire, Sweden. We calculated the information entropy and causal effect through the reaction network and pre-defined scenarios. Then we conducted root cause analysis to determine the quantitative impact of each factor on the variations in methane production rates. We also conducted centrality analysis to find the stability, nodes (i.e., substrates, microbial biomass, and temperature), and edges (i.e., microbial reactions) with more interactions and higher weights in the reaction network. Our results showed that temperature plays a vital role in regulating both the pathways and rates of methanogenesis. With increased temperature, the dominant methane production pathway switched from acetoclastic to hydrogenotrophic, probably due to the thermodynamic limitation at lower temperatures. This finding has significant implications for isotopic methane emission. Concurrently, the importance of microbial biomass increases with warmer temperatures. Importantly, the addition of density-dependent microbial mortality parameterization, potentially more reflective of real-world processes, further increased the importance of microbial biomass on methane production rates.