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I develop a framework for separately identifying direct effects, network-mediated effects, and their interaction when treatment itself changes social connections. The main identifying restriction is that pretreatment networks and covariates capture the common causes of network formation and outcomes.

Randomized saturation experiments measure how outcomes respond to own treatment and to the proportion assigned treatment. When treatment changes social connections, these comparisons do not reveal how much of the response operates through network changes. I propose a framework that decomposes the outcome difference between treated individuals and untreated individuals at different saturations into direct, network-mediated, treatment–network interaction, and saturation-spillover components without imposing functional-form restrictions on potential outcomes. I give conditions under which these components can be identified leveraging pretreatment networks and covariates, without estimating a network formation model. The main restriction, baseline network sufficiency, requires that network-formation factors contain no further information about the shocks governing outcomes at fixed treatment and exposure after conditioning on baseline characteristics. Under neighborhood stability and regularity conditions, I propose an estimation procedure based on debiased machine learning without sample splitting that accommodates network dependence. I propose an inference procedure that conditions on realized saturations, which is valid as cluster sizes grow while the number of clusters stays fixed. Reanalyzing an agronomy-training experiment in Rwanda, I find a negative network-mediated effect on input use for untreated farmers and a positive treatment–network interaction; these opposing effects reveal a network channel obscured by the small total effect and suggest that training changes how social connections shape the allocation of shared inputs.