Estimating Causal Effects with the Neural Autoregressive Density Estimator
This work addresses causal inference for researchers in fields like statistics and machine learning, but it is incremental as it adapts existing neural methods to a known framework.
The paper tackled the problem of estimating causal effects in non-linear systems by using neural autoregressive density estimators within Pearl's do-calculus framework, and it demonstrated that the approach can retrieve causal effects from synthetic data without explicitly modeling variable interactions.
Estimation of causal effects is fundamental in situations were 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 given conditional dependencies. In this paper, 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 the 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.