Estimating causal relations between two or more variables is an important topic in psychology. Establishing a causal relation between two variables can help us in answering that question of why something happens. However, using solely observational data are insufficient to get the complete causal picture. The combination of observational and experimental data may give adequate information to properly estimate causal relations. In this study, we consider the conditions where estimating causal relations might work and we show how well different algorithms, namely the Peter and Clark algorithm, the Downward Ranking of Feed-Forward Loops algorithm, the Transitive Reduction for Weighted Signed Digraphs algorithm, the Invariant Causal Prediction (ICP) algorithm and the Hidden Invariant Causal Prediction (HICP) algorithm, determine causal relations in a simulation study. Results showed that the ICP and the HICP algorithms perform best in most simulation conditions. We also apply every algorithm to an empirical example to show the similarities and differences between the algorithms. We believe that the combination of the ICP and the HICP algorithm may be suitable to be used in future research.

中文翻译：

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因果关系的搜索：因果推理图的不同技术的比较。

估计两个或多个变量之间的因果关系是心理学的一个重要课题。在两个变量之间建立因果关系可以帮助我们回答为什么会发生的问题。然而，仅使用观测数据不足以获得完整的因果图景。观察和实验数据的结合可以提供足够的信息来正确估计因果关系。在这项研究中，我们考虑了估计因果关系可能起作用的条件，并展示了不同算法的效果，即 Peter 和 Clark 算法、前馈循环的向下排序算法、加权有符号有向图算法的传递约简、不变量因果预测（ICP）算法和隐藏不变因果预测（HICP）算法，在模拟研究中确定因果关系。结果表明，ICP 和 HICP 算法在大多数模拟条件下表现最好。我们还将每个算法应用于一个经验示例，以显示算法之间的异同。我们认为 ICP 和 HICP 算法的结合可能适合在未来的研究中使用。