We consider the problem of designing affirmative action policies for selecting the top-k candidates from a pool of applicants. We assume that for each candidate we have socio-demographic attributes and a series of variables that serve as indicators of future performance (e.g., results on standardized tests) - as well as historical data including the actual performance of previously selected candidates. We consider the case where an organization wishes to increase the selection of people from disadvantaged socio-demographic groups. Hence, we seek to design an affirmative action policy to select candidates who are more likely to perform well, but in a way that increases the representation of disadvantaged groups. Our motivating application is the design of university admission policies to bachelor's degrees. We use a causal framework to describe several families of policies (changing component weights, giving bonuses, and enacting quotas), and compare them both theoretically and through extensive experimentation on a real-world dataset containing thousands of university applicants. Our empirical results indicate that simple policies could favor the admission of disadvantaged groups without significantly compromising on the quality of accepted candidates.