Supplemental Material for The Search for Causality: A Comparison of Different Techniques for Causal Inference Graphs
Psychological Methods · 2021
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. Translational AbstractPsychologists study the (possible) causal relation between psychological constructs, like sleep, concentration, and feelings of guilt.For example, does sleep deprivation lead to concentration problems?And could sleep deprivation be caused by increased feelings of guilt?Knowing what the cause is of something so intrusive as sleep problems may in turn lead to finding the solution to help an individual with sleep problems.If we know what causes a problem, we can help to solve it.The type of data that is most often used to estimate causal relations between variables are observational data.These are (empirical) data in which no manipulations have taken place.Although one can use observational data to estimate some causal relations, this alone is not enough to properly estimate all relationships between variables.We also need so-called experimental data to estimate causal relations.These are (empirical) data where some perturbation or manipulation has taken place.Here, we provide an overview of a set of 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, and investigate how well each of these algorithms estimates causal relations by means of a simulation study.We also apply these algorithms to an empirical dataset.Our results showed that two algorithms, the ICP and the HICP-algorithms, perform best in most simulation conditions.We expect that the combination of these algorithms may be suitable to be used in future research.