# Causal Expectation-Maximisation

@inproceedings{Zaffalon2020CausalE, title={Causal Expectation-Maximisation}, author={Marco Zaffalon and Alessandro Antonucci and Rafael Caba{\~n}as}, year={2020} }

Structural causal models are the basic modelling unit in Pearl’s causal theory; in principle they allow us to solve counterfactuals, which are at the top rung of the ladder of causation. But they often contain latent variables that limit their application to special settings. This appears to be a consequence of the fact, proven in this paper, that causal inference is NP-hard even in models characterised by polytree-shaped graphs. To deal with such a hardness, we introduce the causal EM…

## One Citation

Partial Counterfactual Identification from Observational and Experimental Data

- Computer ScienceArXiv
- 2021

It is shown that all counterfactual distributions in an arbitrary structural causal model (SCM) could be generated by a canonical family of SCMs with the same causal diagram where unobserved (exogenous) variables are discrete with a finite domain.

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