Causal Inference Test
Permutation-Based FDR and Confidence Interval
Causal Inference Test
Causal Inference Test for a Binary Outcome
Causal Inference Test for a Continuous Outcome
Omnibus FDR Values for CIT
Parametric tail-area FDR Values, q-values
Nonparametric permutation-based tail-area FDR Values, q-values
Intersection/Union Q-Value
F Test for Linear Model
A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. Described in Millstein, Chen, and Breton (2016), <DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.