@inproceedings{
galhotra2024intervention,
title={Intervention and Conditioning in Causal Bayesian Networks},
author={sainyam galhotra and Joseph Halpern},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=DC28Fpk76s}
}
Causal models are crucial for understanding complex systems and
identifying causal relationships among variables. Even though causal
models are extremely popular, conditional probability calculation of
formulas involving interventions pose significant challenges.
In case of Causal Bayesian Networks (CBNs), Pearl assumes autonomy 
of mechanisms that determine interventions to calculate a range of
probabilities. We show that by making simple yet
often realistic independence assumptions, it is possible 
to uniquely estimate the probability of an interventional formula (including
the well-studied notions of probability of sufficiency and necessity). 
We discuss when these assumptions are appropriate.
Importantly, in many cases of interest, when the assumptions are appropriate,
these probability estimates can be evaluated using
observational data, which carries immense significance in scenarios
where conducting experiments is impractical or unfeasible.
Intervention and Conditioning in Causal Bayesian Networks
sainyam galhotra Joseph Halpern