This paper reports the results of a field experiment to assess the collaborative effects of community participation in the Ugandan oil and gas sector. Our research design assesses collaborative impacts as relational between community members and different decision-makers in the sector and measures these impacts from the point of view of local people. Local people often face power imbalances in collaborative governance. Decision-makers are increasingly attempting to mitigate such imbalances to improve outcomes for communities, but little experimental evidence exists showing the impact of such efforts. Using multilevel ordered logit models, we estimate positive treatment effects, finding that encouraging the equitable participation of communities improves collaboration with other actors. Next, we use machine-learning techniques to demonstrate a method for targeting communities most likely to benefit from the intervention. We estimate that purposefully targeting communities that would benefit most yields a treatment effect about twice as large, relative to pure random assignment. Our results provide evidence that interventions mindful of community needs can improve collaborative governance and shows how such communities can be most effectively targeted. The experiment took place across 107 villages (53 treatment and 54 control) and the unit of statistical analysis is the household, where we report outcomes measured from 6,062 household surveys (approximately half at baseline and half at endline).