The exponential spread of the COVID-19 pandemic has caused countries to impose drastic measures on the public including social distancing, movement restrictions and lockdowns. These government interventions have led to different mobility patterns for the populations. We propose a method of causal inference using community mobility datasets to determine the treatment effects of government interventions on population mobility related outcomes. We first identify the changepoint based on the data of government interventions. We also perform changepoint detection to verify that there is indeed a changepoint at the time of intervention. Then we estimate the mobility trends using a Bayesian structural causal model and project the counterfactual. This is compared to the actual values after interventions to give the treatment effect of interventions. As a specific example, we analyze mobility trends in India before and after interventions. Our analysis shows that there are significant changes in mobility due to government interventions. Our paper aims to provide insights into changes in response to government measures and we hope that it is helpful to those making critical decisions to combat COVID-19.