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Constraint-based inference algorithms for structural models with latent confounders— empirical application and simulations

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Summary

Graphical methods for the discovery of structural models from observational data provide interesting tools for applied researchers. A problem often faced in empirical studies is the presence of latent confounders which produce associations between the observed variables. Although causal inference algorithms exist which can cope with latent confounders, empirical applications assessing the performance of such algorithms are largely lacking. In this study, we apply the constraint based Fast Causal Inference algorithm implemented in the software program TETRAD on a data set containing strategy and performance information about 608 business units. In contrast to the informative and reasonable results for the impirical data, simulation findings reveal problems in recovering some of the structural relations.

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Notes

  1. 1 Meanwhile TETRAD III and a new Java version TETRAD IV are freely available from the TETRAD project homepage http://www.phil.cmu.edu/projects/tetrad/. Since the search algorithms have been slightly improved in this newer versions (email correspondence with Richard Scheines), all analyses reported in this study have been performed with TETRAD III.

  2. 2 We thank an anonymous reviewer for suggesting these simulations.

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Acknowledgements

The author thanks the two anonymous reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. Institute of Marketing, Humboldt-University Berlin, Spandauer Str. 1, D-10178, Berlin

    Dirk Temme

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Appendix

Appendix

Table 6 Results of the Monte Carlo simulation — Sample size
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Table 7 Results of the Monte Carlo simulation — Effect size
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Temme, D. Constraint-based inference algorithms for structural models with latent confounders— empirical application and simulations. Computational Statistics 21, 151–182 (2006). https://doi.org/10.1007/s00180-006-0257-8

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