The causes and consequences of food environment factors such as food insecurity, poverty, unemployment and obesity in the United States are complex. Once causality patterns with regards to these variables are identified, it is important to recognize front-door (Pearl, 2000) and back-door paths (Pearl, 2000) associated with these variables to make sensible and credible policy decisions. These policy interventions are known as performing do-Calculus (Pearl 2000, Spirtes et al., 2000) in causality literature. In this study we use the complex interactions of four food environment variables in the United States (food insecurity, poverty, unemployment and obesity) estimated using artificial intelligence and directed acyclic graphs by Dharmasena, Bessler and Capps (2016) and perform several policy interventions, recognizing front-door and back-door paths. Such policy simulations are vital for agencies not only to design appropriate policies for food assistance, poverty alleviation, combating food insecurity and obesity, but also to recognize effects of policy prior to the desired intervention. Preliminary analysis shows that there are two front-door paths from income to food insecurity, via poverty and via unemployment. Also, there is a front-door path from poverty to food insecurity, while there is an important back- door path from poverty to food insecurity via unemployment.