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In our data-driven world, large amounts of data are collected in all kinds of environments. That is why data analysis rises in importance. How different variables influence each other is a significant part of knowledge discovery and allows strategic decisions based on this knowledge. Therefore, high-quality dependency estimation should be accessible to a variety of people. Many dependency estimation algorithms are difficult to use in a real-world setting. In addition, most of these dependency estimation algorithms need large data sets to return a good estimation. In practice, gathering this amount of data may be costly, especially when collected in experiments with high costs for materials or infrastructure needed. I will do a comparison of different state-of-the-art dependency estimation algorithms. A list of 14 different criteria will be used to determine how promising the algorithm is. This study focuses especially on data efficiency and uncertainty of the dependency estimation algorithms. An algorithm with a high data efficiency can give a good estimation with a small amount of data. A degree of uncertainty helps to interpret the result of the estimator. This allows better decision-making in practice. The comparison includes a theoretical analysis and different experiments with dependency estimation algorithms that performed well in the theoretical analysis.
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