The causal effect of an exposure in an observational study cannot be estimated directly if the confounding variables are not controlled. Inverse probability weighting (IPW) approach can be used to adjust for confounding variables and provide unbiased estimates of the exposure's causal effect. For cluster-structured data, there may be unmeasured cluster-level confounders, and inverse conditional probability weighting (ICPW) approach can provide robust estimation. Double robust estimation combines an outcome model with a model for the exposure (propensity score) to estimate causal effect of an exposure. If at least one of the two models are correctly specified, the estimator will be unbiased. In this paper, the usage of IPW, ICPW and double robust approaches will be illustrated with an application study (the effect of prior bovine viral diarrhea exposure on bovine respiratory disease). The results from the simulation study showed that the IPW, ICPW and double robust approaches would provide more accurate estimation of exposure effect.