2019

Learning Neural Causal Models from Unknown Interventions

Ke, Nan Rosemary, Bilaniuk, Olexa, Goyal, Anirudh et al.

Understand

Promising results have driven a recent surge of interest in continuous optimization methods for Bayesian network structure learning from observational data.

However, there are theoretical limitations on the identifiability of underlying structures obtained from observational data alone.
Interventional data provides much richer information about the underlying data-generating process.
However, the extension and application of methods designed for observational data to include interventions is not straightforward and remains an open problem.

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