Causality

A Critical View of the Structural Causal Model

On 1 Mar, 2021 By admin 0 Comments

February, 2020

Abstract

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.

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Causal Inference: What If

On 19 Feb, 2021 By admin 0 Comments

2020

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).

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