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(2010) Epsa epistemology and methodology of science, Dordrecht, Springer.
Can graphical causal inference be extended to nonlinear settings?
Nadine Chlaß, Alessio Moneta
pp. 63-72
This paper assesses an extension of the method for graphical causal inference proposed by Spirtes et al. and Pearl to nonlinear settings. We propose nonparametric tests for conditional independence based on kernel density estimation and study their relative performance in a Monte Carlo study. Our method outperforms Fischer's z test for nonlinear settings while subject to the so-called curse of dimensionality. We do show, however, how the latter can be overcome by using local bootstrapping.
Publication details
DOI: 10.1007/978-90-481-3263-8_6
Full citation:
Chlaß, N. , Moneta, A. (2010)., Can graphical causal inference be extended to nonlinear settings?, in M. Surez, M. Dorato & M. Rédei (eds.), Epsa epistemology and methodology of science, Dordrecht, Springer, pp. 63-72.
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