Estimating causal effects with the neural autoregressive density estimator

The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional relationship between variables entailed by the graph conditional dependencies. In this article, we deviate from the common assumption of linear relationships in causal models by making use of neural autoregressive density estimators and use them to estimate causal effects within Pearl's do-calculus framework. Using synthetic data, we show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables and include confidence bands using the non-parametric bootstrap. We also explore scenarios that deviate from the ideal causal effect estimation setting such as poor data support or unobserved confounders.

Automated summary

In this article, the authors deviate from linear relationships in causal models by using neural autoregressive density estimators to estimate causal effects. They demonstrate that this approach can retrieve causal effects from non-linear systems without explicitly modeling interactions between variables, and they include confidence bands using non-parametric bootstrap. The authors also explore scenarios deviating from ideal causal effect estimation settings, such as poor data support or unobserved confounders.