Direct Effects Multiplicative Inference (DEMI): causal prediction from pairwise clinical relationships, applied to antidepressant response.

OBJECTIVE: This paper introduces Direct Effects Multiplicative Inference (DEMI), a causal algorithm that reasons through a medical knowledgebase and predicts clinical outcomes from direct, unmediated effects. This study compares performance of DEMI to logistic regression, a common inference mechanism available for analysis of patient-level tabular data. METHODS: We extracted 430 million relationships between pairs of medical concepts from the All of Us database. DEMI uses this knowledgebase to predict likelihood of outcomes, in three steps: (1) patient features are temporally ordered using pairwise precedence counts; (2) each feature's total effect on the outcome is recursively decomposed into direct and indirect (mediated) components by working backward through the temporal order; (3) direct effects are aggregated multiplicatively to estimate the odds of outcomes. DEMI uses the distribution of a pair of variables, as reported in the medical knowledgebase. Logistic regression uses patient-level tabular data, which includes the joint distribution of all variables. Performance was assessed using Area under the Receiver Operating Characteristic curve (AUC). RESULTS: DEMI predicted response to 15 antidepressants with a mean AUC of 0.934 (range, 0.512-0.996). Logistic regression achieved similar discrimination (mean AUC, 0.935; range, 0.512-1.000). At α = 0.05, the two methods did not differ significantly. DEMI achieved comparable discrimination using precomputed pairwise statistics without access to the joint distribution of patient features. Under the same variable-selection rule, DEMI could use on average 113 more predictors per task than logistic regression. CONCLUSION: DEMI predicted antidepressant response from a medical knowledgebase as well as logistic regression did from patient-level data. Under its structural assumptions (no unmeasured confounding and correct temporal ordering), it does so by isolating direct, unmediated predictors and excluding effects that operate through intermediate variables.