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Research Frontiers in Causal Inference in Marketing Experimentation

Application of advanced causal inference methods including instrumental variables and synthetic control approaches to isolate true treatment effects in marketing interventions.

Interference Spillovers in Multi-Touch Attribution Networks

Causal Heterogeneity Across Customer Lifecycle Stages

Temporal Confounding in Long-Horizon Marketing Effects

Platform-Induced Selection Bias in Digital Experiments

Synthetic Control Methods for Brand-Level Interventions

Unobserved Preference Dynamics in Quasi-Experimental Settings

Causal Discovery in High-Dimensional Ad Ecosystem Data

Mediation Effects Through Algorithmic Recommendation Pathways

Instrumental Variables for Organic vs Paid Channel Separation

Contextual Treatment Effect Variation in Real-Time Bidding

All Marketing Science PhD categories