The article introduces a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data, based on a large-scale causal network derived from the Ingenuity Knowledge Base. These tools, implemented within Ingenuity Pathway Analysis (IPA), aim to elucidate the upstream biological causes and probable downstream effects on cellular and organismal biology. The algorithms include Upstream Regulator Analysis (URA), Mechanistic Networks (MN), Causal Network Analysis (CNA), and Downstream Effects Analysis (DEA). URA identifies likely upstream regulators connected to dataset genes, MN extends URA by connecting regulators likely part of the same signaling or causal mechanism, CNA generalizes URA to include paths involving more than one link, and DEA predicts downstream functional effects and phenotypes. The methods are validated through real-world use cases, demonstrating their effectiveness in interpreting gene-expression data and predicting biological mechanisms. The tools are available for use in IPA, a commercial software package.The article introduces a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data, based on a large-scale causal network derived from the Ingenuity Knowledge Base. These tools, implemented within Ingenuity Pathway Analysis (IPA), aim to elucidate the upstream biological causes and probable downstream effects on cellular and organismal biology. The algorithms include Upstream Regulator Analysis (URA), Mechanistic Networks (MN), Causal Network Analysis (CNA), and Downstream Effects Analysis (DEA). URA identifies likely upstream regulators connected to dataset genes, MN extends URA by connecting regulators likely part of the same signaling or causal mechanism, CNA generalizes URA to include paths involving more than one link, and DEA predicts downstream functional effects and phenotypes. The methods are validated through real-world use cases, demonstrating their effectiveness in interpreting gene-expression data and predicting biological mechanisms. The tools are available for use in IPA, a commercial software package.