Understanding whether one event increases or decreases the likelihood of another is critical for real-life applications. Unlike other relationships, promoting and inhibiting relationships capture directional, probabilistic, and context-dependent shifts in event likelihood. A central challenge is to estimate this relative influence from observational data: naive conditional probabilities conflate influence with correlation and are easily distorted by shared contextual confounders. We propose EPIR, a unified framework for estimating promoting and inhibiting relationships from observed event data. EPIR formulates influence as a relative directional effect under comparable contextual conditions, and models event context using : (i) observable history captured and (ii) latent multi-hop propagation mechanisms. EPIR combines context-conditioned predictive evidence with schema-based structural evidence to produce a single signed influence score, where the sign determines promotion versus inhibition. Experiments on real-world datasets show that EPIR outperforms state-of-the-art baselines in accuracy.