The field of biodiversity conservation increasingly recognizes the need for empirical evaluations of conservation
interventions. While the amount of money invested into conservation and the number of protected
areas across the globe have been increasing in the past few decades, few well-designed empirical studies
try to show what could have happened in the absence of the conservation efforts. In this paper,
we propose an empirical method to evaluate such conservation intervention. We integrate a cellular
automata-Markov modeling approach and a counterfactual approach showing what may have happened
in the absence of a certain conservation intervention.Wetest this method in a human-dominated tropical
landscape in Central India; however, our method is transferable to any other socio-ecological setting. The
study area is located in the tropical forests of Central India and has witnessed several management strategies
since its declaration as a protected area in the mid-1970s. However, landscape practitioners have
identified the revised forest policy of 1988 to play a vital role in this landscape. We chose this particular
forest policy as the conservation intervention in our study and tested our method using this landscape
as a template. We used remote sensing and GIS techniques to analyze multi-sensor satellite data from
the last 30 years to monitor forest transitions and compare them with the counterfactual scenario. Our
findings not only shed light on the importance of national-level policies in governing forest dynamics,
but also established our proposed method as an effective tool for empirical evaluation of conservation
intervention, particularly in developing countries.