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Propensity score based adjustments are popular in analyzing observational data. To obtain valid causal estimates, we often assume that the sample is a simple random sample from the population of interest or that the treatment effect is homogeneous across the population. When data from surveys with complex design are used, ad-hoc adjustments to incorporate survey weights are often applied without rigorous justification. In this dissertation, we propose a super population framework, which includes a pair of potential outcomes for every unit in the population, to streamline the propensity score analysis for complex survey data. Based on the proposed framework, we develop propensity score stratification, weighting, and matching estimators along with a new class of hybrid estimators and corresponding variance estimators that adjust for survey design features. Additionally, we argue that in this context we should estimate the propensity scores by a weighted logistic regression using the sampling weights. Various estimators are compared in simulation studies that calculate the bias, mean-squared error, and coverage of the estimators. As the treatment effect becomes more heterogeneous, the gains of adjusting for the survey design increase. Lastly, we demonstrate the proposed methods using a real data example that estimates the effect of health insurance on self-rated health for adults in Ohio who may be eligible for tax credits to purchase medical insurance from the healthcare insurance exchange.