In the univariate case, we show that by comparing the individual complexities of univariate cause and effect, one can identify the cause and the effect, without considering their interaction at all. In our framework, complexities are captured by the reconstruction error of an autoencoder that operates on the quantiles of the distribution. Comparing the reconstruction errors of the two autoencoders, one for each variable, is shown to perform surprisingly well on the accepted causality directionality benchmarks.
Judea Pearl - Computer Science and Statistics, University of California, Los Angeles, USA
Madelyn Glymour - Philosophy, Carnegie Mellon University, Pittsburgh, USA
Nicholas P. Jewell - Biostatistics and Statistics, University of California, Berkeley, USA
Miquel Hernan and Jamie Robins
The book is divided in three parts of increasing difficulty:
Part I is about causal inference without models (i.e., nonparametric identification of causal effects),
Part II is about causal inference with models (i.e., estimation of causal effects with parametric models).
Part III is about causal inference from complex longitudinal data (i.e., estimation of causal effects of time-varying treatments).