Because of the substantive role played by livestock in the income and asset portfolios of the poor, livestock diseases can be an important threat to livelihoods. Yet for a variety of reasons, there are few applicable methods and consequently scant literature to assess the impacts of livestock diseases on livelihood outcomes. Existing literature comprises small-area studies and computable models with wider geographic focus, both of which have limitations in this specific context. We propose an alternative approach for estimating the impacts of livestock diseases on livelihoods. This proposed approach is an adaptation of a quasi-experimental impact evaluation method, namely propensity score matching, which uses features available in large-scale datasets with wide geographic coverage to create counterfactual scenarios that could mimic outcomes of a disease outbreak. By its construction the method is well suited for ex ante impact assessment. As an illustration we apply the method to the hypothetical case of an avian flu outbreak in Kenya.