AI RESEARCH
Distributional Causal Mediation via Conditional Generative Modeling
arXiv CS.LG
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ArXi:2605.01765v1 Announce Type: cross Mediation analysis has traditionally focused on outcome-level summary contrasts, such as mean effects, which may obscure substantial distributional changes induced by complex and nonlinear causal mechanisms. We propose Distributional Causal Mediation Analysis (DCMA), a generative learning framework for identifying and estimating treatment effects on entire outcome distributions transmitted through multiple mediators.